From ca287b33fefa70941c08517ec91111e6ce524468 Mon Sep 17 00:00:00 2001
From: johannes wasmer <johannes.wasmer@gmail.com>
Date: Mon, 3 Jul 2023 15:17:51 +0200
Subject: [PATCH] update bibliography to current Zotero library status
---
bib/bibliography.bib | 2602 ++++++++++++++++++++++++++++++++++++++----
1 file changed, 2396 insertions(+), 206 deletions(-)
diff --git a/bib/bibliography.bib b/bib/bibliography.bib
index 81d85a3..8f9d736 100644
--- a/bib/bibliography.bib
+++ b/bib/bibliography.bib
@@ -1,3 +1,41 @@
+@article{acharMachineLearningElectron2023,
+ title = {Machine {{Learning Electron Density Prediction Using Weighted Smooth Overlap}} of {{Atomic Positions}}},
+ author = {Achar, Siddarth K. and Bernasconi, Leonardo and Johnson, J. Karl},
+ date = {2023-01},
+ journaltitle = {Nanomaterials},
+ volume = {13},
+ number = {12},
+ pages = {1853},
+ publisher = {{Multidisciplinary Digital Publishing Institute}},
+ issn = {2079-4991},
+ doi = {10.3390/nano13121853},
+ url = {https://www.mdpi.com/2079-4991/13/12/1853},
+ urldate = {2023-06-30},
+ abstract = {Having access to accurate electron densities in chemical systems, especially for dynamical systems involving chemical reactions, ion transport, and other charge transfer processes, is crucial for numerous applications in materials chemistry. Traditional methods for computationally predicting electron density data for such systems include quantum mechanical (QM) techniques, such as density functional theory. However, poor scaling of these QM methods restricts their use to relatively small system sizes and short dynamic time scales. To overcome this limitation, we have developed a deep neural network machine learning formalism, which we call deep charge density prediction (DeepCDP), for predicting charge densities by only using atomic positions for molecules and condensed phase (periodic) systems. Our method uses the weighted smooth overlap of atomic positions to fingerprint environments on a grid-point basis and map it to electron density data generated from QM simulations. We trained models for bulk systems of copper, LiF, and silicon; for a molecular system, water; and for two-dimensional charged and uncharged systems, hydroxyl-functionalized graphane, with and without an added proton. We showed that DeepCDP achieves prediction R2 values greater than 0.99 and mean squared error values on the order of 10−5e2 Å−6 for most systems. DeepCDP scales linearly with system size, is highly parallelizable, and is capable of accurately predicting the excess charge in protonated hydroxyl-functionalized graphane. We demonstrate how DeepCDP can be used to accurately track the location of charges (protons) by computing electron densities at a few selected grid points in the materials, thus significantly reducing the computational cost. We also show that our models can be transferable, allowing prediction of electron densities for systems on which it has not been trained but that contain a subset of atomic species on which it has been trained. Our approach can be used to develop models that span different chemical systems and train them for the study of large-scale charge transport and chemical reactions.},
+ issue = {12},
+ langid = {english},
+ keywords = {AML,BLYP,charge transfer,CP2K,DeepCDP,DNN,FCNN,GGA,grid-based descriptors,linear scaling,materials,ML,ML-DFT,ML-ESM,MLP,molecules,PBE,prediction of electron density,pseudopotential,PyTorch,SOAP,transfer learning,weighted SOAP},
+ file = {/Users/wasmer/Nextcloud/Zotero/Achar et al_2023_Machine Learning Electron Density Prediction Using Weighted Smooth Overlap of.pdf}
+}
+
+@article{aguadoMajoranaQubitsTopological2020,
+ title = {Majorana Qubits for Topological Quantum Computing},
+ author = {Aguado, Ramón and Kouwenhoven, Leo P.},
+ date = {2020-06-01},
+ journaltitle = {Physics Today},
+ shortjournal = {Physics Today},
+ volume = {73},
+ number = {6},
+ pages = {44--50},
+ issn = {0031-9228},
+ doi = {10.1063/PT.3.4499},
+ url = {https://doi.org/10.1063/PT.3.4499},
+ urldate = {2023-05-03},
+ abstract = {Although physicists have observed neutrinos for more than 60 years, whether Majorana’s hypothesis is true remains unclear. For example, the discovery of neutrino oscillations, which earned Takaaki Kajita and Arthur McDonald the 2015 Nobel Prize in Physics, demonstrates that neutrinos have mass. But the standard model requires that neutrinos be massless, so various possibilities have been hypothesized to explain the discrepancy. One answer could come from massive neutrinos that do not interact through the weak nuclear force. Such sterile neutrinos could be the particles that Majorana predicted. Whereas conclusive evidence for the existence of Majorana neutrinos remains elusive, researchers are now using Majorana’s idea for other applications, including exotic excitations in superconductors.},
+ keywords = {Majorana,physics,popular science,quantum computing,review,topological insulator},
+ file = {/Users/wasmer/Nextcloud/Zotero/Aguado_Kouwenhoven_2020_Majorana qubits for topological quantum computing.pdf;/Users/wasmer/Zotero/storage/6WK7SWUL/Majorana-qubits-for-topological-quantum.html}
+}
+
@software{AimhubioAim2021,
title = {Aimhubio/Aim},
date = {2021-05-13T14:14:05Z},
@@ -29,6 +67,20 @@
file = {/Users/wasmer/Nextcloud/Zotero/Ai et al_2021_OCELOT.pdf;/Users/wasmer/Zotero/storage/DW64W25V/5.html}
}
+@online{akhmerovCourseOCWTopology2017,
+ type = {MOOC},
+ title = {Course - {{OCW}} - {{Topology}} in {{Condensed Matter}} 2017},
+ author = {Akhmerov, Anton},
+ date = {2017-01-03},
+ url = {https://ocw.tudelft.nl/courses/topology-condensed-matter-concept/},
+ urldate = {2023-06-14},
+ abstract = {Applications of topology in condensed matter based on bulk-edge correspondence. Special attention to the most active research topics in topological condensed matter: theory of topological insulators and Majorana fermions, topological classification of “grand ten” symmetry classes, and topological quantum computation},
+ langid = {american},
+ organization = {{TU Delft OCW}},
+ keywords = {/unread,Chern insulator,course,course material,Hall effect,Hall QSHE,learning material,Majorana,MOOC,online course,physics,quantum computing,theory,topological insulator,TRS},
+ file = {/Users/wasmer/Zotero/storage/YHURA66E/topology-condensed-matter-concept.html}
+}
+
@article{alberi2019MaterialsDesign2018,
title = {The 2019 Materials by Design Roadmap},
author = {Alberi, Kirstin and Nardelli, Marco Buongiorno and Zakutayev, Andriy and Mitas, Lubos and Curtarolo, Stefano and Jain, Anubhav and Fornari, Marco and Marzari, Nicola and Takeuchi, Ichiro and Green, Martin L. and Kanatzidis, Mercouri and Toney, Mike F. and Butenko, Sergiy and Meredig, Bryce and Lany, Stephan and Kattner, Ursula and Davydov, Albert and Toberer, Eric S. and Stevanovic, Vladan and Walsh, Aron and Park, Nam-Gyu and Aspuru-Guzik, Alán and Tabor, Daniel P. and Nelson, Jenny and Murphy, James and Setlur, Anant and Gregoire, John and Li, Hong and Xiao, Ruijuan and Ludwig, Alfred and Martin, Lane W. and Rappe, Andrew M. and Wei, Su-Huai and Perkins, John},
@@ -142,6 +194,24 @@
file = {/Users/wasmer/Nextcloud/Zotero/Anderson et al_2019_Cormorant.pdf;/Users/wasmer/Zotero/storage/RY359LWP/1906.html}
}
+@article{andrejevicMachineLearningSpectralIndicators2022,
+ title = {Machine-{{Learning Spectral Indicators}} of {{Topology}}},
+ author = {Andrejevic, Nina and Andrejevic, Jovana and Bernevig, B. Andrei and Regnault, Nicolas and Han, Fei and Fabbris, Gilberto and Nguyen, Thanh and Drucker, Nathan C. and Rycroft, Chris H. and Li, Mingda},
+ date = {2022},
+ journaltitle = {Advanced Materials},
+ volume = {34},
+ number = {49},
+ pages = {2204113},
+ issn = {1521-4095},
+ doi = {10.1002/adma.202204113},
+ url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202204113},
+ urldate = {2023-06-12},
+ abstract = {Topological materials discovery has emerged as an important frontier in condensed matter physics. While theoretical classification frameworks have been used to identify thousands of candidate topological materials, experimental determination of materials’ topology often poses significant technical challenges. X-ray absorption spectroscopy (XAS) is a widely used materials characterization technique sensitive to atoms’ local symmetry and chemical bonding, which are intimately linked to band topology by the theory of topological quantum chemistry (TQC). Moreover, as a local structural probe, XAS is known to have high quantitative agreement between experiment and calculation, suggesting that insights from computational spectra can effectively inform experiments. In this work, computed X-ray absorption near-edge structure (XANES) spectra of more than 10 000 inorganic materials to train a neural network (NN) classifier that predicts topological class directly from XANES signatures, achieving F1 scores of 89\% and 93\% for topological and trivial classes, respectively is leveraged. Given the simplicity of the XAS setup and its compatibility with multimodal sample environments, the proposed machine-learning-augmented XAS topological indicator has the potential to discover broader categories of topological materials, such as non-cleavable compounds and amorphous materials, and may further inform field-driven phenomena in situ, such as magnetic field-driven topological phase transitions.},
+ langid = {english},
+ keywords = {/unread,AML,ML,prediction of topology,spectroscopy,topological insulator},
+ file = {/Users/wasmer/Zotero/storage/FKYVLXZH/Andrejevic et al. - 2022 - Machine-Learning Spectral Indicators of Topology.pdf;/Users/wasmer/Zotero/storage/H4YA3ND4/adma.html}
+}
+
@online{angelopoulosPredictionPoweredInference2023,
title = {Prediction-{{Powered Inference}}},
author = {Angelopoulos, Anastasios N. and Bates, Stephen and Fannjiang, Clara and Jordan, Michael I. and Zrnic, Tijana},
@@ -158,6 +228,19 @@
file = {/Users/wasmer/Nextcloud/Zotero/Angelopoulos et al_2023_Prediction-Powered Inference.pdf;/Users/wasmer/Zotero/storage/VUQUZZ32/2301.html}
}
+@unpublished{antogninisilvaMaterialsQuantumComputing2023,
+ title = {Materials for Quantum Computing : {{Magnetic}} Impurities Embedded in Superconductors from First Principles},
+ author = {Antognini Silva, David and Rüßmann, Philipp and Blügel, Stefan},
+ date = {2023-05-22},
+ url = {https://srv3.key4events.com/key4register/AbstractList.aspx?e=31&preview=1&aig=-1&ai=3187},
+ urldate = {2023-05-10},
+ abstract = {In the last decades, immense technological and scientific progress was made thanks to the increasing available calculation power provided by the exponential growth of processor capability. However, the miniaturization of transistors is reaching the physical limits of classical processor architectures. In the future, the next big leap for scientific computing is expected to come from the realization of quantum computers. Making more performant quantum computing platforms requires to overcome challenges of decoherence and dephasing of the qubits that form the building blocks for quantum computers. Topological protection is a viable way towards the realization of fault tolerant qubits. Materials that combine magnetism, spin-orbit interaction and conventional s-wave superconductivity are a suitable platform to study Majorana zero modes (MZM) [1], that can be used as building blocks for fault-tolerant topological qubits. In general, magnetic impurities in superconductors leads to localized Yu-Shiba-Rusinov (YSR) states at the impurity [2]. Understanding their interplay with MZMs is crucial to achieve topological quantum computers in the future. In our work, we implemented the Bogoliubov-de Gennes (BdG) formalism in the juKKR Korringa-Kohn-Rostoker Green function impurity code [3] to allow the material-specific description of defects perfectly embedded in superconductors from first principles. We apply it to an Fe impurity embedded in bulk Pb in the normal and superconducting state, then analyze the YSR states of different magnetic transition-metal adatoms placed on a superconducting Nb(110) surface where the influence of the impurity-substrate distance on the energy of the YSR states is discussed. [1] Nadj-Perge et al., Science 346, 6209 (2014). [2] L. Yu, Acta Physica Sinica 21, 75 (1965); H. Shiba, Prog. Theor. Phys. 40, 435 (1968); A. I. Rusinov, Sov. J. Exp. Theor. Phys. 29, 1101 (1969). [3] https://iffgit.fz-juelich.de/kkr/jukkr},
+ eventtitle = {?},
+ venue = {{?}},
+ keywords = {/unread},
+ file = {/Users/wasmer/Zotero/storage/83GMAQZZ/AbstractList.html}
+}
+
@article{artrithBestPracticesMachine2021,
title = {Best Practices in Machine Learning for Chemistry},
author = {Artrith, Nongnuch and Butler, Keith T. and Coudert, François-Xavier and Han, Seungwu and Isayev, Olexandr and Jain, Anubhav and Walsh, Aron},
@@ -196,11 +279,11 @@
file = {/Users/wasmer/Nextcloud/Zotero/Artrith et al_2017_Efficient and accurate machine-learning interpolation of atomic energies in.pdf;/Users/wasmer/Zotero/storage/77VRNTN7/Artrith et al. - 2017 - Efficient and accurate machine-learning interpolat.pdf;/Users/wasmer/Zotero/storage/RL7TSVEA/PhysRevB.96.html}
}
-@online{AssessingDataScience,
+@online{AssessingDataScience2020,
title = {Assessing Data Science Research via Data Science Maturity Levels: {{Patterns}}},
+ date = {2020-04-10},
url = {https://www.cell.com/patterns/dsml},
urldate = {2023-04-13},
- keywords = {/unread},
file = {/Users/wasmer/Zotero/storage/EMN72WHE/dsml.html}
}
@@ -247,6 +330,22 @@
file = {/Users/wasmer/Nextcloud/Zotero/Bac et al_2022_Topological response of the anomalous Hall effect in MnBi2Te4 due to magnetic.pdf;/Users/wasmer/Nextcloud/Zotero/Bac et al_2022_Topological response of the anomalous Hall effect in MnBi2Te4 due to magnetic2_supplementary.pdf;/Users/wasmer/Zotero/storage/E6I5UGGJ/s41535-022-00455-5.html}
}
+@online{balestrieroCookbookSelfSupervisedLearning2023,
+ title = {A {{Cookbook}} of {{Self-Supervised Learning}}},
+ author = {Balestriero, Randall and Ibrahim, Mark and Sobal, Vlad and Morcos, Ari and Shekhar, Shashank and Goldstein, Tom and Bordes, Florian and Bardes, Adrien and Mialon, Gregoire and Tian, Yuandong and Schwarzschild, Avi and Wilson, Andrew Gordon and Geiping, Jonas and Garrido, Quentin and Fernandez, Pierre and Bar, Amir and Pirsiavash, Hamed and LeCun, Yann and Goldblum, Micah},
+ date = {2023-04-24},
+ eprint = {2304.12210},
+ eprinttype = {arxiv},
+ eprintclass = {cs},
+ doi = {10.48550/arXiv.2304.12210},
+ url = {http://arxiv.org/abs/2304.12210},
+ urldate = {2023-05-15},
+ abstract = {Self-supervised learning, dubbed the dark matter of intelligence, is a promising path to advance machine learning. Yet, much like cooking, training SSL methods is a delicate art with a high barrier to entry. While many components are familiar, successfully training a SSL method involves a dizzying set of choices from the pretext tasks to training hyper-parameters. Our goal is to lower the barrier to entry into SSL research by laying the foundations and latest SSL recipes in the style of a cookbook. We hope to empower the curious researcher to navigate the terrain of methods, understand the role of the various knobs, and gain the know-how required to explore how delicious SSL can be.},
+ pubstate = {preprint},
+ keywords = {best practices,computer vision,contrastive learning,data augmentation,Deep learning,General ML,guidelines,image data,ML,pretrained models,SimCLR,SSL,transformer,tutorial},
+ file = {/Users/wasmer/Nextcloud/Zotero/Balestriero et al_2023_A Cookbook of Self-Supervised Learning.pdf;/Users/wasmer/Zotero/storage/RRIMP57I/2304.html}
+}
+
@article{bankoFastTrackResearchData2020,
title = {Fast-{{Track}} to {{Research Data Management}} in {{Experimental Material Science}}–{{Setting}} the {{Ground}} for {{Research Group Level Materials Digitalization}}},
author = {Banko, Lars and Ludwig, Alfred},
@@ -353,7 +452,7 @@
url = {http://arxiv.org/abs/1502.01366},
urldate = {2021-07-06},
abstract = {We present a swift walk-through of our recent work that uses machine learning to fit interatomic potentials based on quantum mechanical data. We describe our Gaussian Approximation Potentials (GAP) framework, discussing a variety of descriptors, how to train the model on total energies and derivatives and the simultaneous use of multiple models. We also show a small example using QUIP, the software sandbox implementation of GAP that is available for non-commercial use.},
- keywords = {_tablet,GAP,ML,models,QUIP,SOAP,tutorial},
+ keywords = {\_tablet,GAP,ML,models,QUIP,SOAP,tutorial},
file = {/Users/wasmer/Nextcloud/Zotero/Bartók_Csányi_2020_Gaussian Approximation Potentials.pdf;/Users/wasmer/Zotero/storage/SBML3RKM/1502.html}
}
@@ -418,7 +517,7 @@
url = {http://arxiv.org/abs/2205.06643},
urldate = {2022-05-21},
abstract = {The rapid progress of machine learning interatomic potentials over the past couple of years produced a number of new architectures. Particularly notable among these are the Atomic Cluster Expansion (ACE), which unified many of the earlier ideas around atom density-based descriptors, and Neural Equivariant Interatomic Potentials (NequIP), a message passing neural network with equivariant features that showed state of the art accuracy. In this work, we construct a mathematical framework that unifies these models: ACE is generalised so that it can be recast as one layer of a multi-layer architecture. From another point of view, the linearised version of NequIP is understood as a particular sparsification of a much larger polynomial model. Our framework also provides a practical tool for systematically probing different choices in the unified design space. We demonstrate this by an ablation study of NequIP via a set of experiments looking at in- and out-of-domain accuracy and smooth extrapolation very far from the training data, and shed some light on which design choices are critical for achieving high accuracy. Finally, we present BOTNet (Body-Ordered-Tensor-Network), a much-simplified version of NequIP, which has an interpretable architecture and maintains accuracy on benchmark datasets.},
- keywords = {_tablet,ACE,BOTNet,descriptors,equivariant,GNN,ML,MLP,MPNN,NequIP,NN,unified theory},
+ keywords = {\_tablet,ACE,BOTNet,descriptors,equivariant,GNN,ML,MLP,MPNN,NequIP,NN,unified theory},
file = {/Users/wasmer/Nextcloud/Zotero/Batatia et al_2022_The Design Space of E(3)-Equivariant Atom-Centered Interatomic Potentials.pdf;/Users/wasmer/Zotero/storage/2FLTPTA2/2205.html}
}
@@ -435,7 +534,7 @@
urldate = {2022-09-25},
abstract = {Creating fast and accurate force fields is a long-standing challenge in computational chemistry and materials science. Recently, several equivariant message passing neural networks (MPNNs) have been shown to outperform models built using other approaches in terms of accuracy. However, most MPNNs suffer from high computational cost and poor scalability. We propose that these limitations arise because MPNNs only pass two-body messages leading to a direct relationship between the number of layers and the expressivity of the network. In this work, we introduce MACE, a new equivariant MPNN model that uses higher body order messages. In particular, we show that using four-body messages reduces the required number of message passing iterations to just \textbackslash emph\{two\}, resulting in a fast and highly parallelizable model, reaching or exceeding state-of-the-art accuracy on the rMD17, 3BPA, and AcAc benchmark tasks. We also demonstrate that using higher order messages leads to an improved steepness of the learning curves.},
pubstate = {preprint},
- keywords = {_tablet,ACE,chemical species scaling problem,descriptors,equivariant,library,MACE,ML,MLP,models,MPNN,Multi-ACE,NequIP,original publication,unified theory,with-code},
+ keywords = {\_tablet,ACE,chemical species scaling problem,descriptors,equivariant,library,MACE,ML,MLP,models,MPNN,Multi-ACE,NequIP,original publication,unified theory,with-code},
file = {/Users/wasmer/Nextcloud/Zotero/Batatia et al_2022_MACE.pdf;/Users/wasmer/Zotero/storage/LDAKZMRF/2206.html}
}
@@ -486,7 +585,7 @@
urldate = {2022-01-02},
abstract = {This work presents Neural Equivariant Interatomic Potentials (NequIP), an E(3)-equivariant neural network approach for learning interatomic potentials from ab-initio calculations for molecular dynamics simulations. While most contemporary symmetry-aware models use invariant convolutions and only act on scalars, NequIP employs E(3)-equivariant convolutions for interactions of geometric tensors, resulting in a more information-rich and faithful representation of atomic environments. The method achieves state-of-the-art accuracy on a challenging and diverse set of molecules and materials while exhibiting remarkable data efficiency. NequIP outperforms existing models with up to three orders of magnitude fewer training data, challenging the widely held belief that deep neural networks require massive training sets. The high data efficiency of the method allows for the construction of accurate potentials using high-order quantum chemical level of theory as reference and enables high-fidelity molecular dynamics simulations over long time scales.},
version = {3},
- keywords = {_tablet,GNN,MD,ML,MLP,molecules,MPNN,NequIP,Neural networks,Physics - Computational Physics,solids},
+ keywords = {\_tablet,GNN,MD,ML,MLP,molecules,MPNN,NequIP,Neural networks,Physics - Computational Physics,solids},
file = {/home/johannes/Nextcloud/Zotero/false;/Users/wasmer/Nextcloud/Zotero/Batzner et al_2021_E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate.pdf;/Users/wasmer/Zotero/storage/85ATGPNR/s41467-022-29939-5.html;/Users/wasmer/Zotero/storage/V4Y8BWNW/2101.html}
}
@@ -501,7 +600,7 @@
abstract = {This thesis is concerned with the quantum mechanical investigation of a novel class of magnetic phenomena in atomic- and nanoscale-sized systems deposited on surfaces or embedded in bulk materials that result from a competition between the exchange and the relativistic spin-orbit interactions. The thesis is motivated by the observation of novel spin-textures of one- and two-dimensional periodicity of nanoscale pitchlength exhibiting a unique winding sense observed in ultra-thin magnetic lms on nonmagnetic metallic substrates with a large spin-orbit interaction. The goal is to extend this eld to magnetic clusters and nano-structures of nite size in order to investigate in how far the size of the cluster and the atoms at the edge of the cluster or ribbon that are particular susceptible to relativistic eects change the balance betweendierent interactions and thus lead to new magnetic phenomena. As an example, the challenging problem of Fe nano-islands on Ir(111) is addressed in detail as for an Fe monolayer on Ir(111) a magnetic nanoskyrmion lattice was observed as magnetic structure.To achieve this goal a new rst-principles all-electron electronic structure code based on density functional theory was developed. The method of choice is the Korringa-Kohn-Rostoker (KKR) impurity Green function method, resorting on a multiple scattering approach. This method has been conceptually further advanced to combine the neglect of any shape approximation to the full potential, with the treatment ofnon-collinear magnetism, of the spin-orbit interaction, as well as of the structural relaxation together with the perfect embedding of a nite size magnetic cluster of atoms into a surface or a bulk environment. For this purpose the formalism makes use of an expansion of the Green function involving explicitly left- and right-hand side scattering solutions. Relativistic eects are treated via the scalar-relativistic approximation and a spin-orbit coupling term treated self-consistently. This required the development of a new algorithm to solve the relativistic quantum mechanical scattering problem for a single atom with a non-spherical potential formulated in terms of the Lippmann-Schwinger integral equation. Prior to the investigation of the Fe nano-islands, the magnetic structure of an Fe monolayer is studied using atomistic spin-dynamics on the basis of a classical model Hamiltonian, which uses realistic coupling parameters obtained from rst principles. It is shown that this method is capable to nd the experimentally determined magnetic structure. [...] Bauer, David Siegfried Georg},
isbn = {9783893369348},
langid = {english},
- keywords = {_tablet,juKKR,KKR,KKRimp,PGI-1/IAS-1,thesis},
+ keywords = {\_tablet,juKKR,KKR,KKRimp,PGI-1/IAS-1,thesis},
file = {/Users/wasmer/Nextcloud/Zotero/Bauer_2014_Development of a relativistic full-potential first-principles multiple.pdf;/Users/wasmer/Zotero/storage/SYS2ZV93/151022.html}
}
@@ -559,7 +658,7 @@
urldate = {2021-05-18},
abstract = {A lot of progress has been made in recent years in the development of atomistic potentials using machine learning (ML) techniques. In contrast to most conventional potentials, which are based on physical approximations and simplifications to derive an analytic functional relation between the atomic configuration and the potential-energy, ML potentials rely on simple but very flexible mathematical terms without a direct physical meaning. Instead, in case of ML potentials the topology of the potential-energy surface is “learned” by adjusting a number of parameters with the aim to reproduce a set of reference electronic structure data as accurately as possible. Due to this bias-free construction, they are applicable to a wide range of systems without changes in their functional form, and a very high accuracy close to the underlying first-principles data can be obtained. Neural network potentials (NNPs), which have first been proposed about two decades ago, are an important class of ML potentials. Although the first NNPs have been restricted to small molecules with only a few degrees of freedom, they are now applicable to high-dimensional systems containing thousands of atoms, which enables addressing a variety of problems in chemistry, physics, and materials science. In this tutorial review, the basic ideas of NNPs are presented with a special focus on developing NNPs for high-dimensional condensed systems. A recipe for the construction of these potentials is given and remaining limitations of the method are discussed. © 2015 Wiley Periodicals, Inc.},
langid = {english},
- keywords = {_tablet,HDNNP,ML,models,molecular dynamics,neural network potentials,review,tutorial},
+ keywords = {\_tablet,HDNNP,ML,models,molecular dynamics,neural network potentials,review,tutorial},
file = {/Users/wasmer/Nextcloud/Zotero/Behler_2015_Constructing high-dimensional neural network potentials.pdf;/Users/wasmer/Zotero/storage/DQEEE6BV/qua.html}
}
@@ -629,7 +728,7 @@
abstract = {Machine learning (ML) promises to tackle the grand challenges in chemistry and speed up the generation, improvement and/or ordering of research hypotheses. Despite the overarching applicability of ML workflows, one usually finds diverse evaluation study designs. The current heterogeneity in evaluation techniques and metrics leads to difficulty in (or the impossibility of) comparing and assessing the relevance of new algorithms. Ultimately, this may delay the digitalization of chemistry at scale and confuse method developers, experimentalists, reviewers and journal editors. In this Perspective, we critically discuss a set of method development and evaluation guidelines for different types of ML-based publications, emphasizing supervised learning. We provide a diverse collection of examples from various authors and disciplines in chemistry. While taking into account varying accessibility across research groups, our recommendations focus on reporting completeness and standardizing comparisons between tools. We aim to further contribute to improved ML transparency and credibility by suggesting a checklist of retro-/prospective tests and dissecting their importance. We envisage that the wide adoption and continuous update of best practices will encourage an informed use of ML on real-world problems related to the chemical sciences.},
issue = {6},
langid = {english},
- keywords = {AML,benchmarking,best practices,chemistry,classification,evaluation metrics,guidelines,MAE,ML,model evaluation,MSE,R2,regression,regression metrics,reproducibility,SHAP,Supervised learning,unsupervised learning,XAI},
+ keywords = {AML,benchmarking,best practices,chemistry,classification,evaluation metrics,guidelines,MAE,ML,model evaluation,model reporting,MSE,R2,regression,regression metrics,reproducibility,SHAP,Supervised learning,unsupervised learning,XAI},
file = {/Users/wasmer/Nextcloud/Zotero/Bender et al_2022_Evaluation guidelines for machine learning tools in the chemical sciences.pdf}
}
@@ -700,6 +799,45 @@
file = {/Users/wasmer/Nextcloud/Zotero/Berner et al_2021_The Modern Mathematics of Deep Learning.pdf;/Users/wasmer/Zotero/storage/XDBSS3FE/2105.html}
}
+@article{bernevigProgressProspectsMagnetic2022,
+ title = {Progress and Prospects in Magnetic Topological Materials},
+ author = {Bernevig, B. Andrei and Felser, Claudia and Beidenkopf, Haim},
+ date = {2022-03},
+ journaltitle = {Nature},
+ volume = {603},
+ number = {7899},
+ pages = {41--51},
+ publisher = {{Nature Publishing Group}},
+ issn = {1476-4687},
+ doi = {10.1038/s41586-021-04105-x},
+ url = {https://www.nature.com/articles/s41586-021-04105-x},
+ urldate = {2023-07-01},
+ abstract = {Magnetic topological materials represent a class of compounds with properties that are strongly influenced by the topology of their electronic wavefunctions coupled with the magnetic spin configuration. Such materials can support chiral electronic channels of perfect conduction, and can be used for an array of applications, from information storage and control to dissipationless spin and charge transport. Here we review the theoretical and experimental progress achieved in the field of magnetic topological materials, beginning with the theoretical prediction of the quantum anomalous Hall effect without Landau levels, and leading to the recent discoveries of magnetic Weyl semimetals and antiferromagnetic topological insulators. We outline recent theoretical progress that has resulted in the tabulation of, for the first time, all magnetic symmetry group representations and topology. We describe several experiments realizing Chern insulators, Weyl and Dirac magnetic semimetals, and an array of axionic and higher-order topological phases of matter, and we survey future perspectives.},
+ issue = {7899},
+ langid = {english},
+ keywords = {/unread,Topological matter},
+ file = {/Users/wasmer/Nextcloud/Zotero/Bernevig et al_2022_Progress and prospects in magnetic topological materials.pdf}
+}
+
+@article{bhardwajTopologicalMaterials2020,
+ title = {Topological {{Materials}}},
+ author = {Bhardwaj, Vishal and Chatterjee, Ratnamala},
+ date = {2020-03-01},
+ journaltitle = {Resonance},
+ shortjournal = {Reson},
+ volume = {25},
+ number = {3},
+ pages = {431--441},
+ issn = {0973-712X},
+ doi = {10.1007/s12045-020-0955-5},
+ url = {https://doi.org/10.1007/s12045-020-0955-5},
+ urldate = {2023-06-14},
+ abstract = {In this article, we provide an overview of the basic concepts of novel topological materials. This new class of materials developed by combining the Weyl/Dirac fermionic electron states and magnetism, provide a materials-science platform to test predictions of the laws of topological physics. Owing to their dissipationless transport, these materials hold high promises for technological applications in quantum computing and spintronics devices.},
+ langid = {english},
+ keywords = {\_tablet,/unread,ARPES,Berry phase,breaking of TRS,Fermi arc,Hall effect,Hall QHE,semimetal,TKNN,topological insulator,TRS},
+ file = {/Users/wasmer/Nextcloud/Zotero/Bhardwaj_Chatterjee_2020_Topological Materials.pdf}
+}
+
@article{bigiSmoothBasisAtomistic2022,
title = {A Smooth Basis for Atomistic Machine Learning},
author = {Bigi, Filippo and Huguenin-Dumittan, Kevin K. and Ceriotti, Michele and Manolopoulos, David E.},
@@ -715,7 +853,7 @@
url = {https://aip.scitation.org/doi/10.1063/5.0124363},
urldate = {2022-12-29},
abstract = {Machine learning frameworks based on correlations of interatomic positions begin with a discretized description of the density of other atoms in the neighborhood of each atom in the system. Symmetry considerations support the use of spherical harmonics to expand the angular dependence of this density, but there is, as of yet, no clear rationale to choose one radial basis over another. Here, we investigate the basis that results from the solution of the Laplacian eigenvalue problem within a sphere around the atom of interest. We show that this generates a basis of controllable smoothness within the sphere (in the same sense as plane waves provide a basis with controllable smoothness for a problem with periodic boundaries) and that a tensor product of Laplacian eigenstates also provides a smooth basis for expanding any higher-order correlation of the atomic density within the appropriate hypersphere. We consider several unsupervised metrics of the quality of a basis for a given dataset and show that the Laplacian eigenstate basis has a performance that is much better than some widely used basis sets and competitive with data-driven bases that numerically optimize each metric. Finally, we investigate the role of the basis in building models of the potential energy. In these tests, we find that a combination of the Laplacian eigenstate basis and target-oriented heuristics leads to equal or improved regression performance when compared to both heuristic and data-driven bases in the literature. We conclude that the smoothness of the basis functions is a key aspect of successful atomic density representations.},
- keywords = {_tablet,/unread,ACDC,ACE,density correlation,descriptor comparison,descriptors,equivariant,general body-order,GPR,Jacobian condition number,Laplacian eigenstate basis,MACE,NN,prediction of potential energy,radial basis,regression,residual variance,smooth basis,Supervised learning,unsupervised learning},
+ keywords = {\_tablet,ACDC,ACE,density correlation,descriptor comparison,descriptors,equivariant,general body-order,GPR,Jacobian condition number,Laplacian eigenstate basis,MACE,NN,prediction of potential energy,radial basis,regression,residual variance,smooth basis,Supervised learning,unsupervised learning},
file = {/home/johannes/Nextcloud/Zotero/false;/Users/wasmer/Nextcloud/Zotero/Bigi et al_2022_A smooth basis for atomistic machine learning.pdf}
}
@@ -732,7 +870,7 @@
urldate = {2023-04-13},
abstract = {Machine-learning models based on a point-cloud representation of a physical object are ubiquitous in scientific applications and particularly well-suited to the atomic-scale description of molecules and materials. Among the many different approaches that have been pursued, the description of local atomic environments in terms of their neighbor densities has been used widely and very succesfully. We propose a novel density-based method which involves computing ``Wigner kernels''. These are fully equivariant and body-ordered kernels that can be computed iteratively with a cost that is independent of the radial-chemical basis and grows only linearly with the maximum body-order considered. This is in marked contrast to feature-space models, which comprise an exponentially-growing number of terms with increasing order of correlations. We present several examples of the accuracy of models based on Wigner kernels in chemical applications, for both scalar and tensorial targets, reaching state-of-the-art accuracy on the popular QM9 benchmark dataset, and we discuss the broader relevance of these ideas to equivariant geometric machine-learning.},
pubstate = {preprint},
- keywords = {ACE,Allegro,AML,benchmarking,body-order,descriptor comparison,descriptors,DimeNet++,equivariant,GPR,KRR,lambda-SOAP,ML,MLP,model comparison,molecules,MPNN,NICE,PAiNN,QM9,representation learning,SA-GPR,SOAP,SphereNet,tensorial target,Wigner kernel},
+ keywords = {\_tablet,ACE,Allegro,AML,benchmarking,body-order,descriptor comparison,descriptors,DimeNet++,equivariant,GPR,KRR,lambda-SOAP,ML,MLP,model comparison,molecules,MPNN,NICE,PAiNN,QM9,representation learning,SA-GPR,SOAP,SphereNet,tensorial target,Wigner kernel},
file = {/Users/wasmer/Nextcloud/Zotero/Bigi et al_2023_Wigner kernels.pdf;/Users/wasmer/Zotero/storage/LERSCPN4/2303.html}
}
@@ -747,7 +885,7 @@
abstract = {Chris Bishop, technical fellow and director of Microsoft Research AI4Science joins colleagues and collaborators across Microsoft Research to discuss how deep learning is set to have a transformational impact on the sciences – including potential applications for drug discovery and materials design. Learn more about the 2022 Microsoft Research Summit […]},
langid = {american},
organization = {{Microsoft Research Summit 2022}},
- keywords = {/unread},
+ keywords = {/unread,AML,emulator,fifth paradigm,geometric deep learning,Microsoft Research,ML,surrogate model},
file = {/Users/wasmer/Zotero/storage/IJ8MX5EV/plenary-the-fifth-paradigm-of-scientific-discovery.html}
}
@@ -790,7 +928,7 @@
abstract = {Blügel, S.; Gompper, G.; Koch, E.; Müller-Krumbhaar, H.; Spatschek, R.; Winkler, R. G.},
isbn = {9783893364305},
langid = {ngerman},
- keywords = {condensed matter,DFT,FZJ,IFF,IFF spring school,KKR,magnetism,PGI-1/IAS-1},
+ keywords = {condensed matter,continuum physics,DFT,electronic structure theory,FZJ,HPC,IFF,IFF spring school,KKR,learning material,magnetism,MD,non-collinear,PGI-1/IAS-1,statistical physics},
file = {/Users/wasmer/Nextcloud/Zotero/Blügel et al_2006_Computational Condensed Matter Physics.pdf;/Users/wasmer/Zotero/storage/IUT3QPKV/56047.html}
}
@@ -805,10 +943,55 @@
abstract = {Blügel, S.},
isbn = {9783893364305},
langid = {english},
- keywords = {_tablet,bluegel,DFT,FLEUR,IFF,IFF spring school,PGI-1/IAS-1,rec-by-bluegel},
+ keywords = {\_tablet,AIMD,bluegel,DFT,FLEUR,IFF,IFF spring school,introduction,learning material,magnetism,PGI-1/IAS-1,rec-by-bluegel},
file = {/Users/wasmer/Nextcloud/Zotero/Blügel_2006_Density Functional Theory in Practice.pdf;/Users/wasmer/Zotero/storage/ZL4WZAY7/51316.html}
}
+@incollection{blugelNoncollinearMagnetismDensity2017,
+ title = {Non-Collinear Magnetism in Density Functional Theory},
+ booktitle = {Non-Collinear Magnetism in Density Functional Theory},
+ author = {Blügel, Stefan},
+ date = {2017},
+ series = {Schriften Des {{Forschungszentrums Jülich Reihe Schlüsseltechnologien}} / {{Key Technologies}}},
+ number = {139},
+ publisher = {{Forschungszentrum Jülich GmbH Zentralbibliothek, Verlag}},
+ location = {{Jülich}},
+ url = {https://juser.fz-juelich.de/record/830530},
+ keywords = {/unread,collinear,DFT,FZJ,IFF,IFF spring school,learning material,magnetism,non-collinear,PGI-1/IAS-1,tutorial}
+}
+
+@book{blugelTopologicalMatterTopological2017,
+ title = {Topological {{Matter}} - {{Topological Insulators}}, {{Skyrmions}} and {{Majoranas}}},
+ author = {Blügel, Stefan and Mokrousov, Yuriy and Schäpers, Thomas and Ando, Yoichi},
+ date = {2017},
+ series = {Schriften Des {{Forschungszentrums Jülich}}. {{Reihe Schlüsseltechnologien}} / {{Key Technologies}}},
+ number = {139},
+ publisher = {{Forschungszentrum Jülich GmbH Zentralbibliothek, Verlag}},
+ location = {{Jülich}},
+ url = {http://hdl.handle.net/2128/22133},
+ abstract = {Condensed matter physics is currently undergoing a revolution through the introduction of concepts arising from topology that are used to characterize physical states, fields and properties from a completely different perspective. With the introduction of topology, the perspective is changed from describing complex systems in terms of local order parameters to a characterization by global quantities, which are measured nonlocally and which endow the systems with a global stability to perturbations. Prominent examples are topological insulators, skyrmions and Majorana fermions. Since topology translates into quantization, and topological order to entanglement, this ongoing revolution has impact on fields like mathematics, materials science, nanoelectronics and quantum information resulting in new device concepts enabling computations without dissipation of energy or enabling the possibility of realizing platforms for topological quantum computation, and ultimately reaching out into applications. Thus, these new exciting scientific developments and their applications are closely related to the grand challenges in information and communication technology and energy saving. Topology is the branch of mathematics that deals with properties of spaces that are invariant under smooth deformations. It provides newly appreciated mathematical tools in condensed matter physics that are currently revolutionizing the field of quantum matter and materials. Topology dictates that if two different Hamiltonians can be smoothly deformed into each other they give rise to many common physical properties and their states are homotopy invariant. Thus, topological invariance, which is often protected by discrete symmetries, provides some robustness that translates into the quantization of properties; such a robust quantization motivates the search and discovery of new topological matter. So far, the mainstream of modern topological condensed matter physics relies on two profoundly different scenarios: the emergence of the complex topology either in real space, as manifested e.g. in non-trivial magnetic structures or in momentum space, finding its realization in such materials as topological and Chern insulators. The latter renowned class of solids attracted considerable attention in recent years owing to its fascinating properties of spin-momentum locking, emergence of topologically protected surface/edge states governed by Dirac physics, as well as the quantization of Hall conductance and the discovery of the quantum spin Hall effect. Historically, the discovery of topological insulators gave rise to the discovery of a whole plethora of topologically non-trivial materials such asWeyl semimetals or topological superconductors, relevant in the context of the realization of Majorana fermions and topological quantum computation. [...]},
+ eventtitle = {Lecture {{Notes}} of the 48th {{IFF Spring School}} 2017},
+ isbn = {978-3-95806-202-3},
+ keywords = {Berry phase,Chern insulator,Chern number,DFT,FZJ,Hall AHE,Hall effect,Hall QAHE,Hall QHE,Hall QSHE,Heisenberg model,IFF,IFF spring school,learning material,magnetic interactions,magnetic materials,magnetic topological materials,Majorana,MZM,PGI,PGI-1/IAS-1,PGI-9,quantum computing,review,skyrmions,spin-dependent,topological,topological insulator,tutorial},
+ file = {/Users/wasmer/Nextcloud/Zotero/Blügel et al_2017_Topological Matter - Topological Insulators, Skyrmions and Majoranas.pdf}
+}
+
+@online{blum-smithMachineLearningInvariant2023,
+ title = {Machine Learning and Invariant Theory},
+ author = {Blum-Smith, Ben and Villar, Soledad},
+ date = {2023-03-25},
+ eprint = {2209.14991},
+ eprinttype = {arxiv},
+ eprintclass = {cs, stat},
+ doi = {10.48550/arXiv.2209.14991},
+ url = {http://arxiv.org/abs/2209.14991},
+ urldate = {2023-06-30},
+ abstract = {Inspired by constraints from physical law, equivariant machine learning restricts the learning to a hypothesis class where all the functions are equivariant with respect to some group action. Irreducible representations or invariant theory are typically used to parameterize the space of such functions. In this article, we introduce the topic and explain a couple of methods to explicitly parameterize equivariant functions that are being used in machine learning applications. In particular, we explicate a general procedure, attributed to Malgrange, to express all polynomial maps between linear spaces that are equivariant under the action of a group \$G\$, given a characterization of the invariant polynomials on a bigger space. The method also parametrizes smooth equivariant maps in the case that \$G\$ is a compact Lie group.},
+ pubstate = {preprint},
+ keywords = {Clebsch-Gordan,equivariant,General ML,GNN,group theory,invariance,invariant theory,ML,ML theory,symmetry,tensor product},
+ file = {/Users/wasmer/Nextcloud/Zotero/Blum-Smith_Villar_2023_Machine learning and invariant theory.pdf;/Users/wasmer/Zotero/storage/K6DZQ29J/2209.html}
+}
+
@book{blumFoundationsDataScience2020,
title = {Foundations of {{Data Science}}},
author = {Blum, Avrim and Hopcroft, John and Kannan, Ravi},
@@ -848,7 +1031,7 @@
volume = {6},
number = {1},
doi = {10.1103/PhysRevMaterials.6.013804},
- keywords = {_tablet,ACE,descriptors,library,ML,pacemaker,with-code},
+ keywords = {\_tablet,ACE,descriptors,library,ML,pacemaker,with-code},
file = {/Users/wasmer/Nextcloud/Zotero/Bochkarev_2022_Efficient parametrization of the atomic cluster expansion.pdf;/Users/wasmer/Zotero/storage/LLPTMRGA/PhysRevMaterials.6.html}
}
@@ -864,7 +1047,7 @@
url = {http://arxiv.org/abs/2205.08177},
urldate = {2022-05-21},
abstract = {Traditionally, interatomic potentials assume local bond formation supplemented by long-range electrostatic interactions when necessary. This ignores intermediate range multi-atom interactions that arise from the relaxation of the electronic structure. Here, we present the multilayer atomic cluster expansion (ml-ACE) that includes collective, semi-local multi-atom interactions naturally within its remit. We demonstrate that ml-ACE significantly improves fit accuracy compared to a local expansion on selected examples and provide physical intuition to understand this improvement.},
- keywords = {_tablet,ACE,descriptors,ML,ml-ACE},
+ keywords = {\_tablet,ACE,descriptors,ML,ml-ACE},
file = {/Users/wasmer/Nextcloud/Zotero/Bochkarev et al_2022_Multilayer atomic cluster expansion for semi-local interactions.pdf;/Users/wasmer/Zotero/storage/NQ2MH8V7/2205.html}
}
@@ -904,6 +1087,20 @@
file = {/Users/wasmer/Nextcloud/Zotero/Bock et al_2021_Hybrid Modelling by Machine Learning Corrections of Analytical Model.pdf;/Users/wasmer/Zotero/storage/IN7CCMRJ/htm.html}
}
+@thesis{bodnarTopologicalDeepLearning2023,
+ title = {Topological {{Deep Learning}}: {{Graphs}}, {{Complexes}}, {{Sheaves}}},
+ shorttitle = {Topological {{Deep Learning}}},
+ author = {Bodnar, Cristian},
+ date = {2023-06-20T12:04:30Z},
+ institution = {{University of Cambridge}},
+ url = {https://www.repository.cam.ac.uk/handle/1810/350982},
+ urldate = {2023-06-22},
+ abstract = {The types of spaces where data resides - graphs, meshes, grids, manifolds - are becoming increasingly varied and heterogeneous. Therefore, translating ideas, models, and theoretical results between different domains is becoming more and more challenging. Nonetheless, two fundamental principles unite all these settings. The first states that data is localised, meaning that data is associated with some regions of the underlying space. The second says that data is relational, and this relational structure reflects how the various regions of the space overlap. It is natural to formalise these axioms using algebraic topology. The "space'' in question is a topological space - a set with a neighbourhood structure - and the data attached to its neighbourhoods are algebraic objects like vector spaces. Since graphs, manifolds and everything in between is a topological space, we adopt this mathematical viewpoint to smoothly transition between domains, improve our theoretical understanding of existent models and design new ones, including for spaces that are yet to be explored in Machine Learning. Guided by this perspective, this work introduces Topological Deep Learning, a research programme studying (deep) models performing inference on data glued to a topological space. This thesis includes four research works expanding upon the directions outlined above. The first work proposes Message Passing Simplicial Networks (MPSNs), a family of models operating on simplicial complexes, a higher-dimensional generalisation of graphs coming from algebraic topology. We study the symmetries these models must satisfy, the topological invariants that describe their behaviour, and how they can learn representations based on discrete differential forms. The second work takes this generalisation further to cell complexes, a class of spaces that also subsume simplicial complexes. We show their additional flexibility benefits molecular applications, where the resulting models outperform prior art on molecular property prediction tasks. The third work proposes a general topological framework for constructing graph coarsening (aka pooling) operators in a way that naturally generalises existing pooling approaches in computer vision. We show that this framework can be used to construct graph-based hierarchical models and visualise attributed graphs. Finally, the last work introduces a new perspective on graph models based on sheaf theory, a subfield of algebraic topology. Sheaves, which are mathematical data structures that naturally store the data attached to a topological space and its relational structure, faithfully realise the axiomatic principles of Topological Deep Learning. We show that sheaf structures on graphs are intimately connected with the asymptotic behaviour of message passing graph models and exploit these connections to design new sheaf-based convolutional architectures. We demonstrate that these models can cope with the challenges of oversmoothing and heterophilic graphs, which affect many existent graph models. Overall, this thesis introduces a novel topological perspective on deep learning for structured data, whose ramifications establish many new connections with algebraic topology.},
+ langid = {english},
+ annotation = {https://doi.org/10.17863/CAM.97212},
+ file = {/Users/wasmer/Nextcloud/Zotero/Bodnar_2023_Topological Deep Learning.pdf}
+}
+
@online{bogojeskiEfficientPrediction3D2018,
title = {Efficient Prediction of {{3D}} Electron Densities Using Machine Learning},
author = {Bogojeski, Mihail and Brockherde, Felix and Vogt-Maranto, Leslie and Li, Li and Tuckerman, Mark E. and Burke, Kieron and Müller, Klaus-Robert},
@@ -916,7 +1113,7 @@
urldate = {2022-07-08},
abstract = {The Kohn-Sham scheme of density functional theory is one of the most widely used methods to solve electronic structure problems for a vast variety of atomistic systems across different scientific fields. While the method is fast relative to other first principles methods and widely successful, the computational time needed is still not negligible, making it difficult to perform calculations for very large systems or over long time-scales. In this submission, we revisit a machine learning model capable of learning the electron density and the corresponding energy functional based on a set of training examples. It allows us to bypass solving the Kohn-Sham equations, providing a significant decrease in computation time. We specifically focus on the machine learning formulation of the Hohenberg-Kohn map and its decomposability. We give results and discuss challenges, limits and future directions.},
pubstate = {preprint},
- keywords = {_tablet,DFT,HK map,ML,ML-DFT,ML-ESM,ML-HK map,molecules,prediction of electron density},
+ keywords = {\_tablet,DFT,HK map,ML,ML-DFT,ML-ESM,ML-HK map,molecules,prediction of electron density},
file = {/Users/wasmer/Nextcloud/Zotero/Bogojeski et al_2018_Efficient prediction of 3D electron densities using machine learning.pdf;/Users/wasmer/Zotero/storage/MCBT39D4/1811.html}
}
@@ -937,7 +1134,7 @@
abstract = {Kohn-Sham density functional theory (DFT) is a standard tool in most branches of chemistry, but accuracies for many molecules are limited to 2-3 kcal~⋅~mol−1 with presently-available functionals. Ab initio methods, such as coupled-cluster, routinely produce much higher accuracy, but computational costs limit their application to small molecules. In this paper, we leverage machine learning to calculate coupled-cluster energies from DFT densities, reaching quantum chemical accuracy (errors below 1 kcal~⋅~mol−1) on test data. Moreover, density-based Δ-learning (learning only the correction to a standard DFT calculation, termed Δ-DFT~) significantly reduces the amount of training data required, particularly when molecular symmetries are included. The robustness of Δ-DFT~ is highlighted by correcting “on the fly” DFT-based molecular dynamics (MD) simulations of resorcinol (C6H4(OH)2) to obtain MD trajectories with coupled-cluster accuracy. We conclude, therefore, that Δ-DFT~ facilitates running gas-phase MD simulations with quantum chemical accuracy, even for strained geometries and conformer changes where standard DFT fails.},
issue = {1},
langid = {english},
- keywords = {_tablet,2-step model,AML,CC,CCSD(T),coupled cluster,Delta,delta learning,DFT,HK map,KKR,ML,ML-DFA,ML-DFT,ML-ESM,ML-HK map,molecules,multi-step model,prediction of electron density,with-code,Δ-machine learning},
+ keywords = {\_tablet,2-step model,AML,CC,CCSD(T),coupled cluster,Delta,delta learning,DFT,HK map,KKR,ML,ML-DFA,ML-DFT,ML-ESM,ML-HK map,molecules,multi-step model,prediction of electron density,with-code,Δ-machine learning},
annotation = {Bandiera\_abtest: a Cc\_license\_type: cc\_by Cg\_type: Nature Research Journals Primary\_atype: Research Subject\_term: Computational chemistry;Computational science Subject\_term\_id: computational-chemistry;computational-science},
file = {/home/johannes/Nextcloud/Zotero/false;/Users/wasmer/Nextcloud/Zotero/Bogojeski et al_2020_Quantum chemical accuracy from density functional approximations via machine.pdf}
}
@@ -991,6 +1188,22 @@
file = {/Users/wasmer/Nextcloud/Zotero/Bornemann_2019_Large-scale Investigations of Non-trivial Magnetic Textures in Chiral Magnets.pdf;/Users/wasmer/Zotero/storage/BZP7D4IW/861845.html}
}
+@online{bosoniHowVerifyPrecision2023,
+ title = {How to Verify the Precision of Density-Functional-Theory Implementations via Reproducible and Universal Workflows},
+ author = {Bosoni, Emanuele and Beal, Louis and Bercx, Marnik and Blaha, Peter and Blügel, Stefan and Bröder, Jens and Callsen, Martin and Cottenier, Stefaan and Degomme, Augustin and Dikan, Vladimir and Eimre, Kristjan and Flage-Larsen, Espen and Fornari, Marco and Garcia, Alberto and Genovese, Luigi and Giantomassi, Matteo and Huber, Sebastiaan P. and Janssen, Henning and Kastlunger, Georg and Krack, Matthias and Kresse, Georg and Kühne, Thomas D. and Lejaeghere, Kurt and Madsen, Georg K. H. and Marsman, Martijn and Marzari, Nicola and Michalicek, Gregor and Mirhosseini, Hossein and Müller, Tiziano M. A. and Petretto, Guido and Pickard, Chris J. and Poncé, Samuel and Rignanese, Gian-Marco and Rubel, Oleg and Ruh, Thomas and Sluydts, Michael and Vanpoucke, Danny E. P. and Vijay, Sudarshan and Wolloch, Michael and Wortmann, Daniel and Yakutovich, Aliaksandr V. and Yu, Jusong and Zadoks, Austin and Zhu, Bonan and Pizzi, Giovanni},
+ date = {2023-05-26},
+ eprint = {2305.17274},
+ eprinttype = {arxiv},
+ eprintclass = {cond-mat, physics:physics},
+ doi = {10.48550/arXiv.2305.17274},
+ url = {http://arxiv.org/abs/2305.17274},
+ urldate = {2023-06-29},
+ abstract = {In the past decades many density-functional theory methods and codes adopting periodic boundary conditions have been developed and are now extensively used in condensed matter physics and materials science research. Only in 2016, however, their precision (i.e., to which extent properties computed with different codes agree among each other) was systematically assessed on elemental crystals: a first crucial step to evaluate the reliability of such computations. We discuss here general recommendations for verification studies aiming at further testing precision and transferability of density-functional-theory computational approaches and codes. We illustrate such recommendations using a greatly expanded protocol covering the whole periodic table from Z=1 to 96 and characterizing 10 prototypical cubic compounds for each element: 4 unaries and 6 oxides, spanning a wide range of coordination numbers and oxidation states. The primary outcome is a reference dataset of 960 equations of state cross-checked between two all-electron codes, then used to verify and improve nine pseudopotential-based approaches. Such effort is facilitated by deploying AiiDA common workflows that perform automatic input parameter selection, provide identical input/output interfaces across codes, and ensure full reproducibility. Finally, we discuss the extent to which the current results for total energies can be reused for different goals (e.g., obtaining formation energies).},
+ pubstate = {preprint},
+ keywords = {ABINIT,AiiDA,AiiDA-FLEUR,benchmarking,best practices,BigDFT,CASTEP,CP2K,DFT,DFT codes comparison,EOS,error estimate,FAIR,FLAPW,FLEUR,GPAW,guidelines,LAPW,library,Materials Cloud,oxides,PAW,pseudopotential,Quantum ESPRESSO,reference dataset,reproducibility,scientific workflows,SIESTA,SIRIUS,VASP,verification,WIEN2k,with-code,workflows},
+ file = {/Users/wasmer/Nextcloud/Zotero/Bosoni et al_2023_How to verify the precision of density-functional-theory implementations via.pdf;/Users/wasmer/Zotero/storage/GIP5Z2MT/2305.html}
+}
+
@article{bouazizSpinDynamics3d2019,
title = {Spin Dynamics of 3d and 4d Impurities Embedded in Prototypical Topological Insulators},
shorttitle = {Spin Dynamics Of},
@@ -1001,7 +1214,7 @@
volume = {3},
number = {5},
doi = {10.1103/PhysRevMaterials.3.054201},
- keywords = {_tablet,defects,Funsilab,impurity embedding,PGI-1/IAS-1,topological insulator},
+ keywords = {\_tablet,defects,Funsilab,impurity embedding,PGI-1/IAS-1,topological insulator},
file = {/Users/wasmer/Nextcloud/Zotero/Bouaziz_2019_Spin dynamics of 3d and 4d impurities embedded in prototypical topological.pdf;/Users/wasmer/Zotero/storage/CW3GMSS2/PhysRevMaterials.3.html}
}
@@ -1095,6 +1308,22 @@
file = {/Users/wasmer/Nextcloud/Zotero/Braun_Ebert_2021_The Impact of Spin–Orbit Interaction on the Image States of High-Z Materials.pdf}
}
+@online{brehmerReducedBasisSurrogates2023,
+ title = {Reduced Basis Surrogates for Quantum Spin Systems Based on Tensor Networks},
+ author = {Brehmer, Paul and Herbst, Michael F. and Wessel, Stefan and Rizzi, Matteo and Stamm, Benjamin},
+ date = {2023-05-13},
+ eprint = {2304.13587},
+ eprinttype = {arxiv},
+ eprintclass = {cond-mat, physics:quant-ph},
+ doi = {10.48550/arXiv.2304.13587},
+ url = {http://arxiv.org/abs/2304.13587},
+ urldate = {2023-05-26},
+ abstract = {Within the reduced basis methods approach, an effective low-dimensional subspace of a quantum many-body Hilbert space is constructed in order to investigate, e.g., the ground-state phase diagram. The basis of this subspace is built from solutions of snapshots, i.e., ground states corresponding to particular and well-chosen parameter values. Here, we show how a greedy strategy to assemble the reduced basis and thus to select the parameter points can be implemented based on matrix-product-states (MPS) calculations. Once the reduced basis has been obtained, observables required for the computation of phase diagrams can be computed with a computational complexity independent of the underlying Hilbert space for any parameter value. We illustrate the efficiency and accuracy of this approach for different one-dimensional quantum spin-1 models, including anisotropic as well as biquadratic exchange interactions, leading to rich quantum phase diagrams.},
+ pubstate = {preprint},
+ keywords = {/unread,Condensed Matter - Strongly Correlated Electrons,Quantum Physics},
+ file = {/Users/wasmer/Nextcloud/Zotero/Brehmer et al_2023_Reduced basis surrogates for quantum spin systems based on tensor networks.pdf;/Users/wasmer/Zotero/storage/KVDI7XMB/2304.html}
+}
+
@article{brockherdeBypassingKohnShamEquations2017,
title = {Bypassing the {{Kohn-Sham}} Equations with Machine Learning},
author = {Brockherde, Felix and Vogt, Leslie and Li, Li and Tuckerman, Mark E. and Burke, Kieron and Müller, Klaus-Robert},
@@ -1112,7 +1341,7 @@
abstract = {Last year, at least 30,000 scientific papers used the Kohn–Sham scheme of density functional theory to solve electronic structure problems in a wide variety of scientific fields. Machine learning holds the promise of learning the energy functional via examples, bypassing the need to solve the Kohn–Sham equations. This should yield substantial savings in computer time, allowing larger systems and/or longer time-scales to be tackled, but attempts to machine-learn this functional have been limited by the need to find its derivative. The present work overcomes this difficulty by directly learning the density-potential and energy-density maps for test systems and various molecules. We perform the first molecular dynamics simulation with a machine-learned density functional on malonaldehyde and are able to capture the intramolecular proton transfer process. Learning density models now allows the construction of accurate density functionals for realistic molecular systems.},
issue = {1},
langid = {english},
- keywords = {_tablet,DFT,HK map,KRR,ML,ML-DFT,ML-ESM,ML-HK map,ML-KS,ML-OF,prediction from potential,prediction of electron density},
+ keywords = {\_tablet,DFT,HK map,KRR,ML,ML-DFT,ML-ESM,ML-HK map,ML-KS,ML-OF,prediction from potential,prediction of electron density},
file = {/Users/wasmer/Nextcloud/Zotero/Brockherde et al_2017_Bypassing the Kohn-Sham equations with machine learning.pdf;/Users/wasmer/Zotero/storage/8X4ALINZ/s41467-017-00839-3.html}
}
@@ -1131,6 +1360,20 @@
keywords = {/unread,AiiDA,AiiDA-FLEUR,DFT,FLEUR,high-throughput computing,PGI-1/IAS-1,thesis}
}
+@report{broholmBasicResearchNeeds2016,
+ title = {Basic {{Research Needs Workshop}} on {{Quantum Materials}} for {{Energy Relevant Technology}}},
+ author = {Broholm, Collin and Fisher, Ian and Moore, Joel and Murnane, Margaret and Moreo, Adriana and Tranquada, John and Basov, Dimitri and Freericks, Jim and Aronson, Meigan and MacDonald, Allan and Fradkin, Eduardo and Yacoby, Amir and Samarth, Nitin and Stemmer, Susanne and Horton, Linda and Horwitz, Jim and Davenport, Jim and Graf, Matthias and Krause, Jeff and Pechan, Mick and Perry, Kelly and Rhyne, Jim and Schwartz, Andy and Thiyagarajan, Thiyaga and Yarris, Lynn and Runkles, Katie},
+ date = {2016-02-10},
+ institution = {{USDOE Office of Science (SC) (United States)}},
+ doi = {10.2172/1616509},
+ url = {https://www.osti.gov/biblio/1616509},
+ urldate = {2023-06-28},
+ abstract = {Imagine future computers that can perform calculations a million times faster than today’s most powerful supercomputers at only a tiny fraction of the energy cost. Imagine power being generated, stored, and then transported across the national grid with nearly no loss. Imagine ultrasensitive sensors that keep us in the loop on what is happening at home or work, warn us when something is going wrong around us, keep us safe from pathogens, and provide unprecedented control of manufacturing and chemical processes. And imagine smart windows, smart clothes, smart buildings, supersmart personal electronics, and many other items — all made from materials that can change their properties “on demand” to carry out the functions we want. The key to attaining these technological possibilities in the 21st century is a new class of materials largely unknown to the general public at this time but destined to become as familiar as silicon. Welcome to the world of quantum materials — materials in which the extraordinary effects of quantum mechanics give rise to exotic and often incredible properties. To realize the tantalizing potential of quantum materials, there is much basic scientific research to be done. Recognizing the high potential impact of quantum materials, nations around the world are already investing in this effort. We must learn how the astonishing properties of quantum materials can be tailored to address our most pressing technological needs, and we must dramatically improve our ability to synthesize, characterize, and control quantum materials. To accelerate the progress of quantum materials research, the U.S. Department of Energy’s Office of Science, Office of Basic Energy Sciences (BES), sponsored a “Basic Research Needs Workshop on Quantum Materials for Energy-relevant Technology,” which was held near Washington, D.C. on February 8–10, 2016. Attended by more than 100 leading national and international scientific experts in the synthesis, characterization, and theory of quantum materials, the workshop identified four priority research directions (PRDs) that will lay the foundation to better understand quantum materials and harness their rich technological potential.},
+ langid = {english},
+ keywords = {/unread,DOE,perspective,physics,popular science,quantum materials,report},
+ file = {/Users/wasmer/Nextcloud/Zotero/Broholm et al_2016_Basic Research Needs Workshop on Quantum Materials for Energy Relevant.pdf}
+}
+
@unpublished{bronsteinGeometricDeepLearning2021,
title = {Geometric {{Deep Learning}}: {{Grids}}, {{Groups}}, {{Graphs}}, {{Geodesics}}, and {{Gauges}}},
shorttitle = {Geometric {{Deep Learning}}},
@@ -1160,7 +1403,7 @@
urldate = {2022-07-10},
abstract = {The purpose of this short essay is to introduce students and other newcomers to the basic ideas and uses of modern electronic density functional theory, including what kinds of approximations are in current use, and how well they work (or not). The complete newcomer should find it orients them well, while even longtime users and aficionados might find something new outside their area. Important questions varying in difficulty and effort are posed in the text, and are answered in the Supporting Information. © 2012 Wiley Periodicals, Inc.},
langid = {english},
- keywords = {_tablet,density functional theory,electronic structure,local density approximation},
+ keywords = {\_tablet,density functional theory,electronic structure,local density approximation},
file = {/home/johannes/Nextcloud/Zotero/false;/Users/wasmer/Nextcloud/Zotero/Burke_Wagner_2013_DFT in a nutshell.pdf;/Users/wasmer/Zotero/storage/CCPHAAVK/qua.html}
}
@@ -1181,6 +1424,26 @@
file = {/Users/wasmer/Nextcloud/Zotero/Burke_Kozlowski_2021_Lies My Teacher Told Me About Density Functional Theory.pdf;/Users/wasmer/Zotero/storage/6EW6SVTP/2108.html}
}
+@article{buskCalibratedUncertaintyMolecular2021,
+ title = {Calibrated Uncertainty for Molecular Property Prediction Using Ensembles of Message Passing Neural Networks},
+ author = {Busk, Jonas and Jørgensen, Peter Bjørn and Bhowmik, Arghya and Schmidt, Mikkel N. and Winther, Ole and Vegge, Tejs},
+ date = {2021-12},
+ journaltitle = {Machine Learning: Science and Technology},
+ shortjournal = {Mach. Learn.: Sci. Technol.},
+ volume = {3},
+ number = {1},
+ pages = {015012},
+ publisher = {{IOP Publishing}},
+ issn = {2632-2153},
+ doi = {10.1088/2632-2153/ac3eb3},
+ url = {https://dx.doi.org/10.1088/2632-2153/ac3eb3},
+ urldate = {2023-06-12},
+ abstract = {Data-driven methods based on machine learning have the potential to accelerate computational analysis of atomic structures. In this context, reliable uncertainty estimates are important for assessing confidence in predictions and enabling decision making. However, machine learning models can produce badly calibrated uncertainty estimates and it is therefore crucial to detect and handle uncertainty carefully. In this work we extend a message passing neural network designed specifically for predicting properties of molecules and materials with a calibrated probabilistic predictive distribution. The method presented in this paper differs from previous work by considering both aleatoric and epistemic uncertainty in a unified framework, and by recalibrating the predictive distribution on unseen data. Through computer experiments, we show that our approach results in accurate models for predicting molecular formation energies with well calibrated uncertainty in and out of the training data distribution on two public molecular benchmark datasets, QM9 and PC9. The proposed method provides a general framework for training and evaluating neural network ensemble models that are able to produce accurate predictions of properties of molecules with well calibrated uncertainty estimates.},
+ langid = {english},
+ keywords = {\_tablet,active learning,AML,ensemble learning,HTC,ML,MLP,MPNN,PC9,QM9,uncertainty quantification},
+ file = {/Users/wasmer/Nextcloud/Zotero/Busk et al_2021_Calibrated uncertainty for molecular property prediction using ensembles of.pdf}
+}
+
@article{bystromCIDERExpressiveNonlocal2022,
title = {{{CIDER}}: {{An Expressive}}, {{Nonlocal Feature Set}} for {{Machine Learning Density Functionals}} with {{Exact Constraints}}},
shorttitle = {{{CIDER}}},
@@ -1235,6 +1498,54 @@
file = {/Users/wasmer/Nextcloud/Zotero/Calderon et al_2015_The AFLOW standard for high-throughput materials science calculations.pdf}
}
+@thesis{callaertCharacterizationDefectsModulations2020,
+ title = {Characterization of Defects, Modulations and Surface Layers in Topological Insulators and Structurally Related Compounds},
+ author = {Callaert, Carolien},
+ date = {2020},
+ institution = {{University of Antwerpen}},
+ url = {https://repository.uantwerpen.be/desktop/irua},
+ urldate = {2023-06-14},
+ keywords = {magnetic doping,thesis,topological insulator},
+ file = {/Users/wasmer/Nextcloud/Zotero/Callaert_2020_Characterization of defects, modulations and surface layers in topological.pdf;/Users/wasmer/Zotero/storage/MYSX7WBQ/irua.html}
+}
+
+@online{callowPhysicsenhancedNeuralNetworks2023,
+ title = {Physics-Enhanced Neural Networks for Equation-of-State Calculations},
+ author = {Callow, Timothy J. and Kraisler, Eli and Cangi, Attila},
+ date = {2023-05-11},
+ eprint = {2305.06856},
+ eprinttype = {arxiv},
+ eprintclass = {physics},
+ doi = {10.48550/arXiv.2305.06856},
+ url = {http://arxiv.org/abs/2305.06856},
+ urldate = {2023-05-26},
+ abstract = {Rapid access to accurate equation-of-state (EOS) data is crucial in the warm-dense matter regime, as it is employed in various applications, such as providing input for hydrodynamics codes to model inertial confinement fusion processes. In this study, we develop neural network models for predicting the EOS based on first-principles data. The first model utilizes basic physical properties, while the second model incorporates more sophisticated physical information, using output from average-atom calculations as features. Average-atom models are often noted for providing a reasonable balance of accuracy and speed; however, our comparison of average-atom models and higher-fidelity calculations shows that more accurate models are required in the warm-dense matter regime. Both the neural network models we propose, particularly the physics-enhanced one, demonstrate significant potential as accurate and efficient methods for computing EOS data in warm-dense matter.},
+ pubstate = {preprint},
+ keywords = {/unread,Physics - Computational Physics,Physics - Plasma Physics},
+ file = {/Users/wasmer/Nextcloud/Zotero/Callow et al_2023_Physics-enhanced neural networks for equation-of-state calculations.pdf;/Users/wasmer/Zotero/storage/W7JDGW6I/2305.html}
+}
+
+@article{cancesNumericalStabilityEfficiency2023,
+ title = {Numerical Stability and Efficiency of Response Property Calculations in Density Functional Theory},
+ author = {Cancès, Eric and Herbst, Michael F. and Kemlin, Gaspard and Levitt, Antoine and Stamm, Benjamin},
+ date = {2023-02},
+ journaltitle = {Letters in Mathematical Physics},
+ shortjournal = {Lett Math Phys},
+ volume = {113},
+ number = {1},
+ eprint = {2210.04512},
+ eprinttype = {arxiv},
+ eprintclass = {cond-mat},
+ pages = {21},
+ issn = {0377-9017, 1573-0530},
+ doi = {10.1007/s11005-023-01645-3},
+ url = {http://arxiv.org/abs/2210.04512},
+ urldate = {2023-05-26},
+ abstract = {Response calculations in density functional theory aim at computing the change in ground-state density induced by an external perturbation. At finite temperature these are usually performed by computing variations of orbitals, which involve the iterative solution of potentially badly-conditioned linear systems, the Sternheimer equations. Since many sets of variations of orbitals yield the same variation of density matrix this involves a choice of gauge. Taking a numerical analysis point of view we present the various gauge choices proposed in the literature in a common framework and study their stability. Beyond existing methods we propose a new approach, based on a Schur complement using extra orbitals from the self-consistent-field calculations, to improve the stability and efficiency of the iterative solution of Sternheimer equations. We show the success of this strategy on nontrivial examples of practical interest, such as Heusler transition metal alloy compounds, where savings of around 40\% in the number of required cost-determining Hamiltonian applications have been achieved.},
+ keywords = {/unread,Condensed Matter - Materials Science,Mathematics - Numerical Analysis},
+ file = {/Users/wasmer/Nextcloud/Zotero/Cancès et al_2023_Numerical stability and efficiency of response property calculations in density.pdf;/Users/wasmer/Zotero/storage/XSX6PBPG/2210.html}
+}
+
@article{cangiPotentialFunctionalsDensity2013,
title = {Potential Functionals versus Density Functionals},
author = {Cangi, Attila and Gross, E. K. U. and Burke, Kieron},
@@ -1249,7 +1560,7 @@
url = {https://link.aps.org/doi/10.1103/PhysRevA.88.062505},
urldate = {2022-07-08},
abstract = {Potential functional approximations are an intriguing alternative to density functional approximations. The potential functional that is dual to the Lieb density functional is defined and its properties are reported. The relationship between the Thomas-Fermi theory as a density functional and the theory as a potential functional is derived. The properties of several recent semiclassical potential functionals are explored, especially regarding their approach to the large particle number and classical continuum limits. The lack of ambiguity in the energy density of potential functional approximations is demonstrated. The density-density response function of the semiclassical approximation is calculated and shown to violate a key symmetry condition.},
- keywords = {_tablet,density functional,density vs potential,DFT,potential},
+ keywords = {\_tablet,density functional,density vs potential,DFT,potential},
file = {/Users/wasmer/Nextcloud/Zotero/Cangi et al_2013_Potential functionals versus density functionals.pdf;/Users/wasmer/Zotero/storage/4U87YYPT/Cangi et al_2013_Potential functionals versus density functionals.pdf;/Users/wasmer/Zotero/storage/AJH43GTS/PhysRevA.88.html}
}
@@ -1302,7 +1613,7 @@
url = {http://arxiv.org/abs/cond-mat/0211443},
urldate = {2021-08-31},
abstract = {This paper is the outgrowth of lectures the author gave at the Physics Institute and the Chemistry Institute of the University of Sao Paulo at Sao Carlos, Brazil, and at the VIII'th Summer School on Electronic Structure of the Brazilian Physical Society. It is an attempt to introduce density-functional theory (DFT) in a language accessible for students entering the field or researchers from other fields. It is not meant to be a scholarly review of DFT, but rather an informal guide to its conceptual basis and some recent developments and advances. The Hohenberg-Kohn theorem and the Kohn-Sham equations are discussed in some detail. Approximate density functionals, selected aspects of applications of DFT, and a variety of extensions of standard DFT are also discussed, albeit in less detail. Throughout it is attempted to provide a balanced treatment of aspects that are relevant for chemistry and aspects relevant for physics, but with a strong bias towards conceptual foundations. The paper is intended to be read before (or in parallel with) one of the many excellent more technical reviews available in the literature.},
- keywords = {_tablet,DFT,learn DFT,review},
+ keywords = {\_tablet,DFT,learn DFT,review},
file = {/Users/wasmer/Nextcloud/Zotero/Capelle_2006_A bird's-eye view of density-functional theory.pdf;/Users/wasmer/Zotero/storage/8TLEU4M3/0211443.html}
}
@@ -1349,10 +1660,24 @@
url = {https://link.aps.org/doi/10.1103/PhysRevB.100.024112},
urldate = {2021-05-13},
abstract = {We explore different ways to simplify the evaluation of the smooth overlap of atomic positions (SOAP) many-body atomic descriptor [Bartók et al., Phys. Rev. B 87, 184115 (2013).]. Our aim is to improve the computational efficiency of SOAP-based similarity kernel construction. While these improved atomic descriptors can be used for general characterization and interpolation of atomic properties, their main target application is accelerated evaluation of machine-learning-based interatomic potentials within the Gaussian approximation potential (GAP) framework [Bartók et al., Phys. Rev. Lett. 104, 136403 (2010)]. We achieve this objective by expressing the atomic densities in an approximate separable form, which decouples the radial and angular channels. We then express the elements of the SOAP descriptor (i.e., the expansion coefficients for the atomic densities) in analytical form given a particular choice of radial basis set. Finally, we derive recursion formulas for the expansion coefficients. This new SOAP-based descriptor allows for tenfold speedups compared to previous implementations, while improving the stability of the radial expansion for distant atomic neighbors, without degradation of the interpolation power of GAP models.},
- keywords = {_tablet,descriptor dimred,descriptors,descriptors analysis,dimensionality reduction,GAP,ML,MLP,SOAP},
+ keywords = {\_tablet,descriptor dimred,descriptors,descriptors analysis,dimensionality reduction,GAP,ML,MLP,SOAP},
file = {/Users/wasmer/Nextcloud/Zotero/Caro_2019_Optimizing many-body atomic descriptors for enhanced computational performance.pdf;/Users/wasmer/Zotero/storage/FDHHHJTR/PhysRevB.100.html}
}
+@report{carterAdvancedResearchDirections2023,
+ title = {Advanced {{Research Directions}} on {{AI}} for {{Science}}, {{Energy}}, and {{Security}}},
+ author = {Carter, Jonathan and Feddema, John and Kothe, Doug and Neely, Rob and Pruet, Jason and Stevens, Rick},
+ date = {2023-06-12},
+ number = {ANL-22/91},
+ institution = {{DOE Office of Science}},
+ url = {https://www.anl.gov/ai-for-science-report},
+ urldate = {2023-06-28},
+ abstract = {Over the past decade, fundamental changes in artificial intelligence (AI) have delivered dramatic insights across a wide breadth of U.S. Department of Energy (DOE) mission space. AI is helping to augment and improve scientific and engineering workflows in national security, the Office of Science, and DOE’s applied energy programs. The progress and potential for AI in DOE science was captured in the 2020 \hspace{0pt}“AI for Science” report. In the short interim, the scale and scope of AI have accelerated, revealing new, emergent properties that yield insights that go beyond enabling opportunities to being potentially transformative in the way that scientific problems are posed and solved. These AI advances also highlight the crucial importance of responsible development of AI, focusing on challenges relating to AI technology (e.g., explainability, validation, security and privacy), implementation (e.g., transparency, safety engineering, ethics), and application (e.g., AI-Human interactions, education, and employment impacts). Under the guidance of both the Office of Science (SC) and the National Nuclear Security Administration (NNSA), the DOE national laboratories organized a series of workshops in 2022 to gather input on new and rapidly emerging opportunities and challenges of scientific AI. This 2023 report is a synthesis of those workshops. The report shows how unique DOE capabilities can enable the community to drive progress in scientific use of AI, building on DOE strengths and investments in computation, data, and communications infrastructure. This report lays out a vision for DOE to leverage and expand new capabilities in AI to accelerate the progress, and deepen the quality of mission areas spanning science, energy, and security. The vision and blueprint align precisely with the pressing need for scientific grounding in areas such as bias, transparency, explainability, security, validation, and the impact of AI on jobs. While dramatic progress being made in AI by industry and defense in the U.S. and other nations, the associated objectives and incentives only partially align with DOE’s mission. This progress also reflects the migration of AI and computer science talent to industry, creating a workforce disruption that DOE must address with urgency. Nevertheless, DOE’s investments in exascale systems, infrastructure, software, theory, and applications—combined with unique, multidisciplinary co-design approaches scaled to thousands of experts—uniquely position the DOE complex to address the challenges of responsible AI and to extend its global leadership in science, energy, and security.},
+ langid = {english},
+ keywords = {AI,Data management,DOE,emulator,exascale,foundation models,GPT,HPC,inverse design,large models,large science facilities,LLM,ML,MLOps,perspective,report,roadmap,RSE,scientific workflows,surrogate model,workflows,zettascale},
+ file = {/Users/wasmer/Nextcloud/Zotero/AI for Science, Energy, and Security Report Argonne National Laboratory.pdf;/Users/wasmer/Zotero/storage/ZCJ2WQX7/ai-for-science-report.html}
+}
+
@article{carvalhoRealspaceMappingTopological2018,
title = {Real-Space Mapping of Topological Invariants Using Artificial Neural Networks},
author = {Carvalho, D. and García-Martínez, N. A. and Lado, J. L. and Fernández-Rossier, J.},
@@ -1421,7 +1746,7 @@
urldate = {2022-12-29},
abstract = {Over the past decade, interatomic potentials based on machine~learning (ML) techniques have become an indispensable tool in the atomic-scale modeling of materials. Trained on energies and forces obtained from electronic-structure calculations, they inherit their predictive accuracy, and extend greatly the length and time scales that are accessible to explicit atomistic simulations. Inexpensive predictions of the energetics of individual configurations have facilitated greatly the calculation of the thermodynamics of materials, including finite-temperature effects and disorder. More recently, ML models have been closing the gap with first-principles calculations in another area: the prediction of arbitrarily complicated functional properties, from vibrational and optical spectroscopies to electronic excitations. The implementation of integrated ML models that combine energetic and functional predictions with statistical and dynamical sampling of atomic-scale properties is bringing the promise of predictive, uncompromising simulations of existing and novel materials closer to its full realization.},
langid = {english},
- keywords = {_tablet,equivariant,Gibbs free energy,integrated models,MLP,multiscale,prediction of DOS,prediction of polarizability,review,symmetry,tensorial target,thermodynamics},
+ keywords = {\_tablet,equivariant,Gibbs free energy,integrated models,MLP,multiscale,prediction of DOS,prediction of polarizability,review,symmetry,tensorial target,thermodynamics},
file = {/Users/wasmer/Nextcloud/Zotero/Ceriotti_2022_Beyond potentials.pdf}
}
@@ -1478,7 +1803,7 @@
urldate = {2023-01-23},
abstract = {Due to the subtle balance of intermolecular interactions that govern structure-property relations, predicting the stability of crystal structures formed from molecular building blocks is a highly non-trivial scientific problem. A particularly active and fruitful approach involves classifying the different combinations of interacting chemical moieties, as understanding the relative energetics of different interactions enables the design of molecular crystals and fine-tuning their stabilities. While this is usually performed based on the empirical observation of the most commonly encountered motifs in known crystal structures, we propose to apply a combination of supervised and unsupervised machine-learning techniques to automate the construction of an extensive library of molecular building blocks. We introduce a structural descriptor tailored to the prediction of the binding (lattice) energy and apply it to a curated dataset of organic crystals and exploit its atom-centered nature to obtain a data-driven assessment of the contribution of different chemical groups to the lattice energy of the crystal. We then interpret this library using a low-dimensional representation of the structure-energy landscape and discuss selected examples of the insights into crystal engineering that can be extracted from this analysis, providing a complete database to guide the design of molecular materials.},
pubstate = {preprint},
- keywords = {_tablet,ACDC,crystal structure,dimensionality reduction,linear regression,organic crystals,PCovR,SOAP,structure prediction,structure search,unsupervised learning},
+ keywords = {\_tablet,ACDC,crystal structure,dimensionality reduction,linear regression,organic crystals,PCovR,SOAP,structure prediction,structure search,unsupervised learning},
file = {/Users/wasmer/Nextcloud/Zotero/Cersonsky et al_2022_A data-driven interpretation of the stability of molecular crystals.pdf;/Users/wasmer/Zotero/storage/25LFMGQ5/2209.html}
}
@@ -1498,10 +1823,23 @@
urldate = {2022-08-10},
abstract = {Selecting the most relevant features and samples out of a large set of candidates is a task that occurs very often in the context of automated data analysis, where it improves the computational performance and often the transferability of a model. Here we focus on two popular subselection schemes applied to this end: CUR decomposition, derived from a low-rank approximation of the feature matrix, and farthest point sampling (FPS), which relies on the iterative identification of the most diverse samples and discriminating features. We modify these unsupervised approaches, incorporating a supervised component following the same spirit as the principal covariates (PCov) regression method. We show how this results in selections that perform better in supervised tasks, demonstrating with models of increasing complexity, from ridge regression to kernel ridge regression and finally feed-forward neural networks. We also present adjustments to minimise the impact of any subselection when performing unsupervised tasks. We demonstrate the significant improvements associated with PCov-CUR and PCov-FPS selections for applications to chemistry and materials science, typically reducing by a factor of two the number of features and samples required to achieve a given level of regression accuracy.},
langid = {english},
- keywords = {_tablet,CUR decomposition,dimensionality reduction,feature selection,FPS,KPCovR,KRR,PCovR,sample selection},
+ keywords = {\_tablet,CUR decomposition,dimensionality reduction,feature selection,FPS,KPCovR,KRR,PCovR,sample selection},
file = {/home/johannes/Nextcloud/Zotero/false;/Users/wasmer/Nextcloud/Zotero/Cersonsky et al_2021_Improving sample and feature selection with principal covariates regression.pdf}
}
+@inproceedings{cesaProgramBuildEquivariant2021,
+ title = {A {{Program}} to {{Build E}}({{N}})-{{Equivariant Steerable CNNs}}},
+ author = {Cesa, Gabriele and Lang, Leon and Weiler, Maurice},
+ date = {2021-10-06},
+ url = {https://openreview.net/forum?id=WE4qe9xlnQw},
+ urldate = {2023-06-30},
+ abstract = {Equivariance is becoming an increasingly popular design choice to build data efficient neural networks by exploiting prior knowledge about the symmetries of the problem at hand. Euclidean steerable CNNs are one of the most common classes of equivariant networks. While the constraints these architectures need to satisfy are understood, existing approaches are tailored to specific (classes of) groups. No generally applicable method that is practical for implementation has been described so far. In this work, we generalize the Wigner-Eckart theorem proposed in Lang \& Weiler (2020), which characterizes general \$G\$-steerable kernel spaces for compact groups \$G\$ over their homogeneous spaces, to arbitrary \$G\$-spaces. This enables us to directly parameterize filters in terms of a band-limited basis on the whole space rather than on \$G\$'s orbits, but also to easily implement steerable CNNs equivariant to a large number of groups. To demonstrate its generality, we instantiate our method on a variety of isometry groups acting on the Euclidean space \$\textbackslash mathbb\{R\}\^3\$. Our framework allows us to build \$E(3)\$ and \$SE(3)\$-steerable CNNs like previous works, but also CNNs with arbitrary \$G\textbackslash leq O(3)\$-steerable kernels. For example, we build 3D CNNs equivariant to the symmetries of platonic solids or choose \$G=SO(2)\$ when working with 3D data having only azimuthal symmetries. We compare these models on 3D shapes and molecular datasets, observing improved performance by matching the model's symmetries to the ones of the data.},
+ eventtitle = {International {{Conference}} on {{Learning Representations}}},
+ langid = {english},
+ keywords = {3D,AML,CNN,computer vision,E(3),E(n),equivariant,General ML,library,ML,molecules,PyTorch,SO(3),steerable CNN,tensor field},
+ file = {/Users/wasmer/Nextcloud/Zotero/Cesa et al_2021_A Program to Build E(N)-Equivariant Steerable CNNs.pdf}
+}
+
@article{chandrasekaranSolvingElectronicStructure2019,
title = {Solving the Electronic Structure Problem with Machine Learning},
author = {Chandrasekaran, Anand and Kamal, Deepak and Batra, Rohit and Kim, Chiho and Chen, Lihua and Ramprasad, Rampi},
@@ -1519,7 +1857,7 @@
abstract = {Simulations based on solving the Kohn-Sham (KS) equation of density functional theory (DFT) have become a vital component of modern materials and chemical sciences research and development portfolios. Despite its versatility, routine DFT calculations are usually limited to a few hundred atoms due to the computational bottleneck posed by the KS equation. Here we introduce a machine-learning-based scheme to efficiently assimilate the function of the KS equation, and by-pass it to directly, rapidly, and accurately predict the electronic structure of a material or a molecule, given just its atomic configuration. A new rotationally invariant representation is utilized to map the atomic environment around a grid-point to the electron density and local density of states at that grid-point. This mapping is learned using a neural network trained on previously generated reference DFT results at millions of grid-points. The proposed paradigm allows for the high-fidelity emulation of KS DFT, but orders of magnitude faster than the direct solution. Moreover, the machine learning prediction scheme is strictly linear-scaling with system size.},
issue = {1},
langid = {english},
- keywords = {_tablet,custom structural descriptors,descriptors,DFT,FCNN,grid-based descriptors,LDOS,ML,ML-DFT,ML-ESM,models,NN,prediction from structure,prediction of electron density,prediction of LDOS,RNN},
+ keywords = {\_tablet,custom structural descriptors,descriptors,DFT,FCNN,grid-based descriptors,LDOS,ML,ML-DFT,ML-ESM,models,NN,prediction from structure,prediction of electron density,prediction of LDOS,RNN},
annotation = {Bandiera\_abtest: a Cc\_license\_type: cc\_by Cg\_type: Nature Research Journals Primary\_atype: Research Subject\_term: Computational methods;Electronic structure;Theory and computation Subject\_term\_id: computational-methods;electronic-structure;theory-and-computation},
file = {/Users/wasmer/Nextcloud/Zotero/Chandrasekaran et al_2019_Solving the electronic structure problem with machine learning.pdf;/Users/wasmer/Nextcloud/Zotero/Chandrasekaran et al_2019_Solving the electronic structure problem with machine learning2.pdf;/Users/wasmer/Zotero/storage/TL92B668/s41524-019-0162-7.html}
}
@@ -1539,6 +1877,28 @@
file = {/Users/wasmer/Nextcloud/Zotero/Chang et al_2013_Experimental Observation of the Quantum Anomalous Hall Effect in a Magnetic.pdf}
}
+@article{changOvercomingDataScarcity2022,
+ title = {Towards Overcoming Data Scarcity in Materials Science: Unifying Models and Datasets with a Mixture of Experts Framework},
+ shorttitle = {Towards Overcoming Data Scarcity in Materials Science},
+ author = {Chang, Rees and Wang, Yu-Xiong and Ertekin, Elif},
+ date = {2022-11-18},
+ journaltitle = {npj Computational Materials},
+ shortjournal = {npj Comput Mater},
+ volume = {8},
+ number = {1},
+ pages = {1--9},
+ publisher = {{Nature Publishing Group}},
+ issn = {2057-3960},
+ doi = {10.1038/s41524-022-00929-x},
+ url = {https://www.nature.com/articles/s41524-022-00929-x},
+ urldate = {2023-06-16},
+ abstract = {While machine learning has emerged in recent years as a useful tool for the rapid prediction of materials properties, generating sufficient data to reliably train models without overfitting is often impractical. Towards overcoming this limitation, we present a general framework for leveraging complementary information across different models and datasets for accurate prediction of data-scarce materials properties. Our approach, based on a machine learning paradigm called mixture of experts, outperforms pairwise transfer learning on 14 of 19 materials property regression tasks, performing comparably on four of the remaining five. The approach is interpretable, model-agnostic, and scalable to combining an arbitrary number of pre-trained models and datasets to any downstream property prediction task. We anticipate the performance of our framework will further improve as better model architectures, new pre-training tasks, and larger materials datasets are developed by the community.},
+ issue = {1},
+ langid = {english},
+ keywords = {AML,catastrophic forgetting,CGCNN,data imbalance,data scarcity,materials,mixture of experts,ML,multitask learning,regression,small data,train-test split,transfer learning,transfer learning pairwise,with-code,with-data},
+ file = {/Users/wasmer/Nextcloud/Zotero/Chang et al_2022_Towards overcoming data scarcity in materials science.pdf}
+}
+
@unpublished{chardDLHubModelData2018,
title = {{{DLHub}}: {{Model}} and {{Data Serving}} for {{Science}}},
shorttitle = {{{DLHub}}},
@@ -1550,7 +1910,7 @@
url = {http://arxiv.org/abs/1811.11213},
urldate = {2022-01-03},
abstract = {While the Machine Learning (ML) landscape is evolving rapidly, there has been a relative lag in the development of the "learning systems" needed to enable broad adoption. Furthermore, few such systems are designed to support the specialized requirements of scientific ML. Here we present the Data and Learning Hub for science (DLHub), a multi-tenant system that provides both model repository and serving capabilities with a focus on science applications. DLHub addresses two significant shortcomings in current systems. First, its selfservice model repository allows users to share, publish, verify, reproduce, and reuse models, and addresses concerns related to model reproducibility by packaging and distributing models and all constituent components. Second, it implements scalable and low-latency serving capabilities that can leverage parallel and distributed computing resources to democratize access to published models through a simple web interface. Unlike other model serving frameworks, DLHub can store and serve any Python 3-compatible model or processing function, plus multiple-function pipelines. We show that relative to other model serving systems including TensorFlow Serving, SageMaker, and Clipper, DLHub provides greater capabilities, comparable performance without memoization and batching, and significantly better performance when the latter two techniques can be employed. We also describe early uses of DLHub for scientific applications.},
- keywords = {Computer Science - Distributed; Parallel; and Cluster Computing,Computer Science - Machine Learning,Statistics - Machine Learning},
+ keywords = {{Computer Science - Distributed, Parallel, and Cluster Computing},Computer Science - Machine Learning,Statistics - Machine Learning},
file = {/Users/wasmer/Nextcloud/Zotero/Chard et al_2018_DLHub.pdf;/Users/wasmer/Zotero/storage/VT5H6PP6/1811.html}
}
@@ -1569,7 +1929,7 @@
url = {https://doi.org/10.1021/acs.chemmater.9b01294},
urldate = {2022-01-02},
abstract = {Graph networks are a new machine learning (ML) paradigm that supports both relational reasoning and combinatorial generalization. Here, we develop universal MatErials Graph Network (MEGNet) models for accurate property prediction in both molecules and crystals. We demonstrate that the MEGNet models outperform prior ML models such as the SchNet in 11 out of 13 properties of the QM9 molecule data set. Similarly, we show that MEGNet models trained on ∼60 000 crystals in the Materials Project substantially outperform prior ML models in the prediction of the formation energies, band gaps, and elastic moduli of crystals, achieving better than density functional theory accuracy over a much larger data set. We present two new strategies to address data limitations common in materials science and chemistry. First, we demonstrate a physically intuitive approach to unify four separate molecular MEGNet models for the internal energy at 0 K and room temperature, enthalpy, and Gibbs free energy into a single free energy MEGNet model by incorporating the temperature, pressure, and entropy as global state inputs. Second, we show that the learned element embeddings in MEGNet models encode periodic chemical trends and can be transfer-learned from a property model trained on a larger data set (formation energies) to improve property models with smaller amounts of data (band gaps and elastic moduli).},
- keywords = {_tablet,GNN,library,MEGNet,molecules,solids,vectorial learning target,with-code},
+ keywords = {\_tablet,GNN,library,MEGNet,molecules,solids,vectorial learning target,with-code},
file = {/Users/wasmer/Nextcloud/Zotero/Chen et al_2019_Graph Networks as a Universal Machine Learning Framework for Molecules and.pdf}
}
@@ -1598,10 +1958,26 @@
url = {http://arxiv.org/abs/2202.02450},
urldate = {2022-03-28},
abstract = {Interatomic potentials (IAPs), which describe the potential energy surface of a collection of atoms, are a fundamental input for atomistic simulations. However, existing IAPs are either fitted to narrow chemistries or too inaccurate for general applications. Here, we report a universal IAP for materials based on graph neural networks with three-body interactions (M3GNet). The M3GNet IAP was trained on the massive database of structural relaxations performed by the Materials Project over the past 10 years and has broad applications in structural relaxation, dynamic simulations and property prediction of materials across diverse chemical spaces. About 1.8 million potentially stable materials were identified from a screening of 31 million hypothetical crystal structures, demonstrating a machine learning-accelerated pathway to the discovery of synthesizable materials with exceptional properties.},
- keywords = {_tablet,condensed matter,GNN,library,M3GNet,materials,materials database,materials project,matterverse,MEGNet,ML,MLP,molecules,periodic table,solids,tensorial target,with-code},
+ keywords = {\_tablet,condensed matter,GNN,library,M3GNet,materials,materials database,materials project,matterverse,MEGNet,ML,MLP,molecules,periodic table,solids,tensorial target,universal potential,with-code},
file = {/Users/wasmer/Nextcloud/Zotero/Chen_Ong_2022_A Universal Graph Deep Learning Interatomic Potential for the Periodic Table.pdf;/Users/wasmer/Zotero/storage/H4FKVKUF/2202.html}
}
+@online{chongRobustnessLocalPredictions2023,
+ title = {Robustness of {{Local Predictions}} in {{Atomistic Machine Learning Models}}},
+ author = {Chong, Sanggyu and Grasselli, Federico and Mahmoud, Chiheb Ben and Morrow, Joe D. and Deringer, Volker L. and Ceriotti, Michele},
+ date = {2023-06-27},
+ eprint = {2306.15638},
+ eprinttype = {arxiv},
+ eprintclass = {cond-mat, physics:physics},
+ doi = {10.48550/arXiv.2306.15638},
+ url = {http://arxiv.org/abs/2306.15638},
+ urldate = {2023-07-01},
+ abstract = {Machine learning (ML) models for molecules and materials commonly rely on a decomposition of the global target quantity into local, atom-centered contributions. This approach is convenient from a computational perspective, enabling large-scale ML-driven simulations with a linear-scaling cost, and also allow for the identification and post-hoc interpretation of contributions from individual chemical environments and motifs to complicated macroscopic properties. However, even though there exist practical justifications for these decompositions, only the global quantity is rigorously defined, and thus it is unclear to what extent the atomistic terms predicted by the model can be trusted. Here, we introduce a quantitative metric, which we call the local prediction rigidity (LPR), that allows one to assess how robust the locally decomposed predictions of ML models are. We investigate the dependence of LPR on the aspects of model training, particularly the composition of training dataset, for a range of different problems from simple toy models to real chemical systems. We present strategies to systematically enhance the LPR, which can be used to improve the robustness, interpretability, and transferability of atomistic ML models.},
+ pubstate = {preprint},
+ keywords = {/unread,Condensed Matter - Materials Science,Physics - Chemical Physics},
+ file = {/Users/wasmer/Nextcloud/Zotero/Chong et al_2023_Robustness of Local Predictions in Atomistic Machine Learning Models2.pdf;/Users/wasmer/Zotero/storage/SEXVHR6B/2306.html}
+}
+
@article{choudharyAtomisticLineGraph2021,
title = {Atomistic {{Line Graph Neural Network}} for Improved Materials Property Predictions},
author = {Choudhary, Kamal and DeCost, Brian},
@@ -1623,6 +1999,19 @@
file = {/Users/wasmer/Nextcloud/Zotero/Choudhary_DeCost_2021_Atomistic Line Graph Neural Network for improved materials property predictions.pdf;/Users/wasmer/Zotero/storage/F8XSYTPV/s41524-021-00650-1.html}
}
+@online{choudharyLargeScaleBenchmark2023,
+ title = {Large {{Scale Benchmark}} of {{Materials Design Methods}}},
+ author = {Choudhary, Kamal and Wines, Daniel and Li, Kangming and Garrity, Kevin F. and Gupta, Vishu and Romero, Aldo H. and Krogel, Jaron T. and Saritas, Kayahan and Fuhr, Addis and Ganesh, Panchapakesan and Kent, Paul R. C. and Yan, Keqiang and Lin, Yuchao and Ji, Shuiwang and Blaiszik, Ben and Reiser, Patrick and Friederich, Pascal and Agrawal, Ankit and Tiwary, Pratyush and Beyerle, Eric and Minch, Peter and Rhone, Trevor David and Takeuchi, Ichiro and Wexler, Robert B. and Mannodi-Kanakkithodi, Arun and Ertekin, Elif and Mishra, Avanish and Mathew, Nithin and Baird, Sterling G. and Wood, Mitchell and Rohskopf, Andrew Dale and Hattrick-Simpers, Jason and Wang, Shih-Han and Achenie, Luke E. K. and Xin, Hongliang and Williams, Maureen and Biacchi, Adam J. and Tavazza, Francesca},
+ date = {2023-06-20},
+ url = {https://arxiv.org/abs/2306.11688v1},
+ urldate = {2023-07-01},
+ abstract = {Lack of rigorous reproducibility and validation are major hurdles for scientific development across many fields. Materials science in particular encompasses a variety of experimental and theoretical approaches that require careful benchmarking. Leaderboard efforts have been developed previously to mitigate these issues. However, a comprehensive comparison and benchmarking on an integrated platform with multiple data modalities with both perfect and defect materials data is still lacking. This work introduces JARVIS-Leaderboard, an open-source and community-driven platform that facilitates benchmarking and enhances reproducibility. The platform allows users to set up benchmarks with custom tasks and enables contributions in the form of dataset, code, and meta-data submissions. We cover the following materials design categories: Artificial Intelligence (AI), Electronic Structure (ES), Force-fields (FF), Quantum Computation (QC) and Experiments (EXP). For AI, we cover several types of input data, including atomic structures, atomistic images, spectra, and text. For ES, we consider multiple ES approaches, software packages, pseudopotentials, materials, and properties, comparing results to experiment. For FF, we compare multiple approaches for material property predictions. For QC, we benchmark Hamiltonian simulations using various quantum algorithms and circuits. Finally, for experiments, we use the inter-laboratory approach to establish benchmarks. There are 1281 contributions to 274 benchmarks using 152 methods with more than 8 million data-points, and the leaderboard is continuously expanding. The JARVIS-Leaderboard is available at the website: https://pages.nist.gov/jarvis\_leaderboard},
+ langid = {english},
+ organization = {{arXiv.org}},
+ keywords = {/unread},
+ file = {/Users/wasmer/Nextcloud/Zotero/Choudhary et al_2023_Large Scale Benchmark of Materials Design Methods.pdf}
+}
+
@article{choudharyMachineLearningForcefieldinspired2018,
title = {Machine Learning with Force-Field-Inspired Descriptors for Materials: {{Fast}} Screening and Mapping Energy Landscape},
shorttitle = {Machine Learning with Force-Field-Inspired Descriptors for Materials},
@@ -1704,7 +2093,7 @@
url = {http://arxiv.org/abs/2201.11591},
urldate = {2022-03-23},
abstract = {Machine-learning generative methods for material design are constructed by representing a given chemical structure, either a solid or a molecule, over appropriate atomic features, generally called structural descriptors. These must be fully descriptive of the system, must facilitate the training process and must be invertible, so that one can extract the atomic configurations corresponding to the output of the model. In general, this last requirement is not automatically satisfied by the most efficient structural descriptors, namely the representation is not directly invertible. Such drawback severely limits our freedom of choice in selecting the most appropriate descriptors for the problem, and thus our flexibility to construct generative models. In this work, we present a general optimization method capable of inverting any local many-body descriptor of the chemical environment, back to a cartesian representation. The algorithm is then implemented together with the bispectrum representation of the local structure and demonstrated for a number of molecules. The scheme presented here, thus, represents a general approach to the inversion of structural descriptors, enabling the construction of efficient structural generative models.},
- keywords = {_tablet,descriptors,generative models,inversion,ML},
+ keywords = {\_tablet,descriptors,generative models,inversion,ML},
file = {/Users/wasmer/Nextcloud/Zotero/Cobelli et al_2022_Inversion of the chemical environment representations.pdf;/Users/wasmer/Zotero/storage/A6MH6ZIG/2201.html}
}
@@ -1741,6 +2130,45 @@
file = {/Users/wasmer/Nextcloud/Zotero/Collins et al_2003_The Human Genome Project.pdf}
}
+@article{coudertFailureReproduceResults2023,
+ title = {Failure to Reproduce the Results of “{{A}} New Transferable Interatomic Potential for Molecular Dynamics Simulations of Borosilicate Glasses”},
+ author = {Coudert, François-Xavier},
+ date = {2023-09-01},
+ journaltitle = {Journal of Non-Crystalline Solids},
+ shortjournal = {Journal of Non-Crystalline Solids},
+ volume = {615},
+ pages = {122423},
+ issn = {0022-3093},
+ doi = {10.1016/j.jnoncrysol.2023.122423},
+ url = {https://www.sciencedirect.com/science/article/pii/S0022309323002892},
+ urldate = {2023-06-30},
+ abstract = {We reproduced the simulations described in Wang et al. (2018) and found we could not obtain the results reported. The root cause was identified to be incorrect atom masses in the original simulation files. As a consequence, the potential does not reproduce the experimental glass density – and presumably, other structural properties – and should be considered with great caution.},
+ langid = {english},
+ keywords = {/unread,AML,glasses,ML,MLP,reproduction study,skepticism},
+ file = {/Users/wasmer/Nextcloud/Zotero/Coudert_2023_Failure to reproduce the results of “A new transferable interatomic potential.pdf;/Users/wasmer/Zotero/storage/GIWCGKS3/S0022309323002892.html}
+}
+
+@article{crisostomoSevenUsefulQuestions2023,
+ title = {Seven {{Useful Questions}} in {{Density Functional Theory}}},
+ author = {Crisostomo, Steven and Pederson, Ryan and Kozlowski, John and Kalita, Bhupalee and Cancio, Antonio C. and Datchev, Kiril and Wasserman, Adam and Song, Suhwan and Burke, Kieron},
+ date = {2023-04-09},
+ journaltitle = {Letters in Mathematical Physics},
+ shortjournal = {Lett Math Phys},
+ volume = {113},
+ number = {2},
+ eprint = {2207.05794},
+ eprinttype = {arxiv},
+ eprintclass = {math-ph, physics:quant-ph},
+ pages = {42},
+ issn = {1573-0530},
+ doi = {10.1007/s11005-023-01665-z},
+ url = {http://arxiv.org/abs/2207.05794},
+ urldate = {2023-05-26},
+ abstract = {We explore a variety of unsolved problems in density functional theory, where mathematicians might prove useful. We give the background and context of the different problems, and why progress toward resolving them would help those doing computations using density functional theory. Subjects covered include the magnitude of the kinetic energy in Hartree-Fock calculations, the shape of adiabatic connection curves, using the constrained search with input densities, densities of states, the semiclassical expansion of energies, the tightness of Lieb-Oxford bounds, and how we decide the accuracy of an approximate density.},
+ keywords = {adiabatic connection,DFT,DOS,HFT,Lieb-Oxford bound,mathematics,open questions,physics,theory,WFT,xc functional},
+ file = {/Users/wasmer/Nextcloud/Zotero/Crisostomo et al_2023_Seven Useful Questions in Density Functional Theory.pdf;/Users/wasmer/Zotero/storage/45PQRMPB/2207.html}
+}
+
@article{cuevas-zuviriaMachineLearningAnalytical2021,
title = {Machine {{Learning}} of {{Analytical Electron Density}} in {{Large Molecules Through Message-Passing}}},
author = {Cuevas-Zuviría, Bruno and Pacios, Luis F.},
@@ -1798,6 +2226,46 @@
file = {/Users/wasmer/Zotero/storage/5MTYTHXV/S0927025612000687.html}
}
+@article{curtaroloHighthroughputHighwayComputational2013,
+ title = {The High-Throughput Highway to Computational Materials Design},
+ author = {Curtarolo, Stefano and Hart, Gus L. W. and Nardelli, Marco Buongiorno and Mingo, Natalio and Sanvito, Stefano and Levy, Ohad},
+ date = {2013-03},
+ journaltitle = {Nature Materials},
+ shortjournal = {Nature Mater},
+ volume = {12},
+ number = {3},
+ pages = {191--201},
+ publisher = {{Nature Publishing Group}},
+ issn = {1476-4660},
+ doi = {10.1038/nmat3568},
+ url = {https://www.nature.com/articles/nmat3568},
+ urldate = {2023-06-30},
+ abstract = {High-throughput computational approaches combining thermodynamic and electronic-structure methods with data mining and database construction are increasingly used to analyse huge amounts of data for the discovery and design of new materials. This Review provides an overall perspective of the field for a broad range of materials, and discusses upcoming challenges and opportunities.},
+ issue = {3},
+ langid = {english},
+ keywords = {AFLOWLIB,Automation,compositional descriptors,Database,descriptor comparison,descriptors,DFT,High-throughput,HTC,materials database,materials discovery,materials screening,review},
+ file = {/Users/wasmer/Nextcloud/Zotero/Curtarolo et al_2013_The high-throughput highway to computational materials design.pdf}
+}
+
+@article{daigavaneUnderstandingConvolutionsGraphs2021,
+ title = {Understanding {{Convolutions}} on {{Graphs}}},
+ author = {Daigavane, Ameya and Ravindran, Balaraman and Aggarwal, Gaurav},
+ date = {2021-09-02},
+ journaltitle = {Distill},
+ shortjournal = {Distill},
+ volume = {6},
+ number = {9},
+ pages = {e32},
+ issn = {2476-0757},
+ doi = {10.23915/distill.00032},
+ url = {https://distill.pub/2021/understanding-gnns},
+ urldate = {2023-06-28},
+ abstract = {Understanding the building blocks and design choices of graph neural networks.},
+ langid = {english},
+ keywords = {/unread,AML,blog,General ML,GNN,graph ML,introduction,learning material,ML,MPNN},
+ file = {/Users/wasmer/Zotero/storage/ACTKG5CV/understanding-gnns.html}
+}
+
@online{damewoodRepresentationsMaterialsMachine2023,
title = {Representations of {{Materials}} for {{Machine Learning}}},
author = {Damewood, James and Karaguesian, Jessica and Lunger, Jaclyn R. and Tan, Aik Rui and Xie, Mingrou and Peng, Jiayu and Gómez-Bombarelli, Rafael},
@@ -1810,7 +2278,7 @@
urldate = {2023-03-19},
abstract = {High-throughput data generation methods and machine learning (ML) algorithms have given rise to a new era of computational materials science by learning relationships among composition, structure, and properties and by exploiting such relations for design. However, to build these connections, materials data must be translated into a numerical form, called a representation, that can be processed by a machine learning model. Datasets in materials science vary in format (ranging from images to spectra), size, and fidelity. Predictive models vary in scope and property of interests. Here, we review context-dependent strategies for constructing representations that enable the use of materials as inputs or outputs of machine learning models. Furthermore, we discuss how modern ML techniques can learn representations from data and transfer chemical and physical information between tasks. Finally, we outline high-impact questions that have not been fully resolved and thus, require further investigation.},
pubstate = {preprint},
- keywords = {/unread,AML,defects,descriptors,disordered,materials,ML,review,review-of-descriptors,TODO},
+ keywords = {\_tablet,/unread,AML,defects,descriptors,disordered,materials,ML,review,review-of-descriptors,TODO},
file = {/Users/wasmer/Nextcloud/Zotero/Damewood et al_2023_Representations of Materials for Machine Learning.pdf;/Users/wasmer/Zotero/storage/7JZ95596/2301.html}
}
@@ -1841,7 +2309,7 @@
url = {http://arxiv.org/abs/2112.13055},
urldate = {2022-01-03},
abstract = {Many atomic descriptors are currently limited by their unfavourable scaling with the number of chemical elements \$S\$ e.g. the length of body-ordered descriptors, such as the Smooth Overlap of Atomic Positions (SOAP) power spectrum (3-body) and the Atomic Cluster Expansion (ACE) (multiple body-orders), scales as \$(NS)\^\textbackslash nu\$ where \$\textbackslash nu+1\$ is the body-order and \$N\$ is the number of radial basis functions used in the density expansion. We introduce two distinct approaches which can be used to overcome this scaling for the SOAP power spectrum. Firstly, we show that the power spectrum is amenable to lossless compression with respect to both \$S\$ and \$N\$, so that the descriptor length can be reduced from \$\textbackslash mathcal\{O\}(N\^2S\^2)\$ to \$\textbackslash mathcal\{O\}\textbackslash left(NS\textbackslash right)\$. Secondly, we introduce a generalized SOAP kernel, where compression is achieved through the use of the total, element agnostic density, in combination with radial projection. The ideas used in the generalized kernel are equally applicably to any other body-ordered descriptors and we demonstrate this for the Atom Centered Symmetry Functions (ACSF). Finally, both compression approaches are shown to offer comparable performance to the original descriptor across a variety of numerical tests.},
- keywords = {_tablet,ACE,ACSF,chemical species scaling problem,descriptor dimred,descriptors,descriptors analysis,dimensionality reduction,library,ML,SOAP,with-code},
+ keywords = {\_tablet,ACE,ACSF,chemical species scaling problem,descriptor dimred,descriptors,descriptors analysis,dimensionality reduction,library,ML,SOAP,with-code},
file = {/Users/wasmer/Nextcloud/Zotero/Darby et al_2021_Compressing local atomic neighbourhood descriptors.pdf;/Users/wasmer/Zotero/storage/GXXQQPAA/2112.html}
}
@@ -1862,7 +2330,7 @@
abstract = {Many atomic descriptors are currently limited by their unfavourable scaling with the number of chemical elements S e.g. the length of body-ordered descriptors, such as the SOAP power spectrum (3-body) and the (ACE) (multiple body-orders), scales as (NS)ν where ν\,+\,1 is the body-order and N is the number of radial basis functions used in the density expansion. We introduce two distinct approaches which can be used to overcome this scaling for the SOAP power spectrum. Firstly, we show that the power spectrum is amenable to lossless compression with respect to both S and N, so that the descriptor length can be reduced from \$\$\{\{\{\textbackslash mathcal\{O\}\}\}\}(\{N\}\^\{2\}\{S\}\^\{2\})\$\$to \$\$\{\{\{\textbackslash mathcal\{O\}\}\}\}\textbackslash left(NS\textbackslash right)\$\$. Secondly, we introduce a generalised SOAP kernel, where compression is achieved through the use of the total, element agnostic density, in combination with radial projection. The ideas used in the generalised kernel are equally applicably to any other body-ordered descriptors and we demonstrate this for the (ACSF).},
issue = {1},
langid = {english},
- keywords = {_tablet,ACE,ACSF,chemical species scaling problem,descriptor dimred,descriptors,descriptors analysis,dimensionality reduction,library,ML,SOAP,with-code},
+ keywords = {\_tablet,ACE,ACSF,chemical species scaling problem,descriptor dimred,descriptors,descriptors analysis,dimensionality reduction,library,ML,SOAP,with-code},
file = {/Users/wasmer/Nextcloud/Zotero/Darby et al_2022_Compressing local atomic neighbourhood descriptors.pdf;/Users/wasmer/Zotero/storage/WR6IJ7MC/s41524-022-00847-y.html}
}
@@ -1878,7 +2346,7 @@
urldate = {2022-10-05},
abstract = {Density based representations of atomic environments that are invariant under Euclidean symmetries have become a widely used tool in the machine learning of interatomic potentials, broader data-driven atomistic modelling and the visualisation and analysis of materials datasets.The standard mechanism used to incorporate chemical element information is to create separate densities for each element and form tensor products between them. This leads to a steep scaling in the size of the representation as the number of elements increases. Graph neural networks, which do not explicitly use density representations, escape this scaling by mapping the chemical element information into a fixed dimensional space in a learnable way. We recast this approach as tensor factorisation by exploiting the tensor structure of standard neighbour density based descriptors. In doing so, we form compact tensor-reduced representations whose size does not depend on the number of chemical elements, but remain systematically convergeable and are therefore applicable to a wide range of data analysis and regression tasks.},
pubstate = {preprint},
- keywords = {_tablet,ACE,chemical species scaling problem,descriptor dimred,descriptors,dimensionality reduction,MACE,ML,Multi-ACE},
+ keywords = {\_tablet,ACE,chemical species scaling problem,descriptor dimred,descriptors,dimensionality reduction,MACE,ML,Multi-ACE},
file = {/Users/wasmer/Nextcloud/Zotero/Darby et al_2022_Tensor-reduced atomic density representations.pdf;/Users/wasmer/Zotero/storage/6XMXCLL4/2210.html}
}
@@ -1979,7 +2447,7 @@
url = {https://doi.org/10.1021/acs.jpca.0c07458},
urldate = {2023-04-13},
abstract = {Computations based on density functional theory (DFT) are transforming various aspects of materials research and discovery. However, the effort required to solve the central equation of DFT, namely the Kohn–Sham equation, which remains a major obstacle for studying large systems with hundreds of atoms in a practical amount of time with routine computational resources. Here, we propose a deep learning architecture that systematically learns the input–output behavior of the Kohn–Sham equation and predicts the electronic density of states, a primary output of DFT calculations, with unprecedented speed and chemical accuracy. The algorithm also adapts and progressively improves in predictive power and versatility as it is exposed to new diverse atomic configurations. We demonstrate this capability for a diverse set of carbon allotropes spanning a large configurational and phase space. The electronic density of states, along with the electronic charge density, may be used downstream to predict a variety of materials properties, bypassing the Kohn–Sham equation, leading to an ultrafast and high-fidelity DFT emulator.},
- keywords = {AML,carbon,defects,disordered,grid-based descriptors,materials,ML,ML-DFT,ML-ESM,NN,prediction of DOS,prediction of electron density,vacancies,VASP},
+ keywords = {\_tablet,AML,carbon,defects,disordered,grid-based descriptors,materials,ML,ML-DFT,ML-ESM,NN,prediction of DOS,prediction of electron density,vacancies,VASP},
file = {/Users/wasmer/Nextcloud/Zotero/del Rio et al_2020_An Efficient Deep Learning Scheme To Predict the Electronic Structure of.pdf;/Users/wasmer/Zotero/storage/46EGTQLS/acs.jpca.html}
}
@@ -2078,7 +2546,7 @@
url = {https://doi.org/10.1021/acs.chemrev.1c00022},
urldate = {2022-06-03},
abstract = {We provide an introduction to Gaussian process regression (GPR) machine-learning methods in computational materials science and chemistry. The focus of the present review is on the regression of atomistic properties: in particular, on the construction of interatomic potentials, or force fields, in the Gaussian Approximation Potential (GAP) framework; beyond this, we also discuss the fitting of arbitrary scalar, vectorial, and tensorial quantities. Methodological aspects of reference data generation, representation, and regression, as well as the question of how a data-driven model may be validated, are reviewed and critically discussed. A survey of applications to a variety of research questions in chemistry and materials science illustrates the rapid growth in the field. A vision is outlined for the development of the methodology in the years to come.},
- keywords = {_tablet,active learning,GAP,GPR,librascal,materials,ML,ML-DFT,ML-ESM,MLP,models,molecules,prediction of electron density,prediction of LDOS,review,SA-GPR,SOAP,structure prediction,structure search,tutorial,with-code},
+ keywords = {\_tablet,active learning,GAP,GPR,librascal,materials,ML,ML-DFT,ML-ESM,MLP,models,molecules,prediction of electron density,prediction of LDOS,review,SA-GPR,SOAP,structure prediction,structure search,tutorial,with-code},
file = {/Users/wasmer/Nextcloud/Zotero/Deringer et al_2021_Gaussian Process Regression for Materials and Molecules.pdf;/Users/wasmer/Zotero/storage/LSTJST2A/acs.chemrev.html}
}
@@ -2194,6 +2662,22 @@
file = {/Users/wasmer/Nextcloud/Zotero/Di Sante et al_2022_Deep Learning the Functional Renormalization Group.pdf;/Users/wasmer/Zotero/storage/LKT2Z79L/Di Sante et al_2022_Deep Learning the Functional Renormalization Group-supp.pdf;/Users/wasmer/Zotero/storage/PGSNSHSM/PhysRevLett.129.html}
}
+@online{dominaClusterExpansionConstructed2023,
+ title = {Cluster Expansion Constructed over {{Jacobi-Legendre}} Polynomials for Accurate Force Fields},
+ author = {Domina, Michelangelo and Patil, Urvesh and Cobelli, Matteo and Sanvito, Stefano},
+ date = {2023-06-26},
+ eprint = {2208.10292},
+ eprinttype = {arxiv},
+ eprintclass = {cond-mat},
+ doi = {10.48550/arXiv.2208.10292},
+ url = {http://arxiv.org/abs/2208.10292},
+ urldate = {2023-06-27},
+ abstract = {We introduce a compact cluster expansion method, constructed over Jacobi and Legendre polynomials, to generate highly accurate and flexible machine-learning force fields. The constituent many-body contributions are separated, interpretable and adaptable to replicate the physical knowledge of the system. In fact, the flexibility introduced by the use of the Jacobi polynomials allows us to impose, in a natural way, constrains and symmetries to the cluster expansion. This has the effect of reducing the number of parameters needed for the fit and of enforcing desired behaviours of the potential. For instance, we show that our Jacobi-Legendre cluster expansion can be designed to generate potentials with a repulsive tail at short inter-atomic distances, without the need of imposing any external function. Our method is here continuously compared with available machine-learning potential schemes, such as the atomic cluster expansion and potentials built over the bispectrum. As an example we construct a Jacobi-Legendre potential for carbon, by training a slim and accurate model capable of describing crystalline graphite and diamond, as well as liquid and amorphous elemental carbon.},
+ pubstate = {preprint},
+ keywords = {ABINIT,ACE,ACE-related,AML,bispectrum,carbon,cluster expansion,DFT,forces,JLP,MD,ML,ML-DFT,ML-FF,MLP,phonon,prediction of electron density},
+ file = {/Users/wasmer/Nextcloud/Zotero/Domina et al_2023_Cluster expansion constructed over Jacobi-Legendre polynomials for accurate.pdf;/Users/wasmer/Zotero/storage/AYU8VHC7/2208.html}
+}
+
@online{dominaJacobiLegendrePotential2022,
title = {The {{Jacobi-Legendre}} Potential},
author = {Domina, Michelangelo and Patil, Urvesh and Cobelli, Matteo and Sanvito, Stefano},
@@ -2206,7 +2690,7 @@
urldate = {2022-09-05},
abstract = {Inspired by the cluster expansion method, we introduce a compact machine-learning potential constructed over Jacobi and Legendre polynomials. The constituent many-body contributions are separated, fully interpretable and adaptable to replicate the physical knowledge of the system, such as a repulsive behaviour at a small inter-atomic distance. Most importantly the potential requires a small number of features to achieve accuracy comparable to that of more numerically heavy and descriptor-rich alternatives. This is here tested for an organic molecule, a crystalline solid and an amorphous compound. Furthermore, we argue that the physical interpretability of the various terms is key to the selection and training of stable potentials.},
pubstate = {preprint},
- keywords = {_tablet,ACE,descriptors,DFT,invariance,Jacobi-Legendre potential,JLP,linear regression,ML,ML-ESM,MLP,prediction of total energy,SNAP},
+ keywords = {\_tablet,ACE,descriptors,DFT,invariance,Jacobi-Legendre potential,JLP,linear regression,ML,ML-ESM,MLP,prediction of total energy,SNAP},
file = {/Users/wasmer/Nextcloud/Zotero/Domina et al_2022_The Jacobi-Legendre potential.pdf;/Users/wasmer/Zotero/storage/DUUKR6TZ/2208.html}
}
@@ -2225,7 +2709,7 @@
url = {https://link.aps.org/doi/10.1103/PhysRevB.105.214439},
urldate = {2022-09-05},
abstract = {We introduce a translational and rotational invariant local representation for vector fields, which can be employed in the construction of machine learning energy models of solids and molecules. This allows us to describe, on the same footing, the energy fluctuations due to the atomic motion, the longitudinal and transverse excitations of the vector field, and their mutual interplay. The formalism can then be applied to physical systems where the total energy is determined by a vector density, as in the case of magnetism. Our representation is constructed over the power spectrum of the combined angular momentum describing the local atomic positions and the vector field, and it can be used in conjunction with different machine learning schemes and data taken from accurate ab initio electronic structure theories. We demonstrate the descriptive power of our representation for a range of classical spin Hamiltonian and machine learning algorithms. In particular, we construct energy models based on both linear Ridge regression, as in conventional spectral neighbor analysis potentials, and the Gaussian approximation. These are both built to represent a Heisenberg-type Hamiltonian including a longitudinal energy term and spin-lattice coupling.},
- keywords = {_tablet,descriptors,DFT,GPR,Heisenberg model,Jij,LRR,magnetism,ML,ML-DFT,ML-ESM,spin-dependent},
+ keywords = {\_tablet,descriptors,DFT,GPR,Heisenberg model,Jij,LRR,magnetism,ML,ML-DFT,ML-ESM,spin-dependent},
file = {/Users/wasmer/Nextcloud/Zotero/Domina et al_2022_Spectral neighbor representation for vector fields.pdf;/Users/wasmer/Zotero/storage/F4KNYWPX/Domina et al_2022_Spectral neighbor representation for vector fields.pdf;/Users/wasmer/Zotero/storage/QX9ZENU5/PhysRevB.105.html}
}
@@ -2240,7 +2724,7 @@
url = {http://arxiv.org/abs/2202.13773},
urldate = {2022-03-23},
abstract = {We introduce a translational and rotational invariant local representation for vector fields, which can be employed in the construction of machine-learning energy models of solids and molecules. This allows us to describe, on the same footing, the energy fluctuations due to the atomic motion, the longitudinal and transverse excitations of the vector field, and their mutual interplay. The formalism can then be applied to physical systems where the total energy is determined by a vector density, as in the case of magnetism. Our representation is constructed over the power spectrum of the combined angular momentum describing the local atomic positions and the vector field, and can be used in conjunction with different machine-learning schemes and data taken from accurate ab initio electronic structure theories. We demonstrate the descriptive power of our representation for a range of classical spin Hamiltonian and machine-learning algorithms. In particular, we construct energy models based on both linear Ridge regression, as in conventional spectral neighbour analysis potentials, and gaussian approximation. These are both built to represent a Heisenberg-type Hamiltonian including a longitudinal energy term and spin-lattice coupling.},
- keywords = {_tablet,descriptors,DFT,GPR,Heisenberg model,Jij,LRR,magnetism,ML,ML-DFT,ML-ESM,spin-dependent},
+ keywords = {\_tablet,descriptors,DFT,GPR,Heisenberg model,Jij,LRR,magnetism,ML,ML-DFT,ML-ESM,spin-dependent},
file = {/Users/wasmer/Nextcloud/Zotero/Domina et al_2022_A spectral-neighbour representation for vector fields.pdf;/Users/wasmer/Zotero/storage/EB6UHPCQ/2202.html}
}
@@ -2363,7 +2847,7 @@
url = {https://link.aps.org/doi/10.1103/PhysRevB.99.014104},
urldate = {2022-05-11},
abstract = {The atomic cluster expansion is developed as a complete descriptor of the local atomic environment, including multicomponent materials, and its relation to a number of other descriptors and potentials is discussed. The effort for evaluating the atomic cluster expansion is shown to scale linearly with the number of neighbors, irrespective of the order of the expansion. Application to small Cu clusters demonstrates smooth convergence of the atomic cluster expansion to meV accuracy. By introducing nonlinear functions of the atomic cluster expansion an interatomic potential is obtained that is comparable in accuracy to state-of-the-art machine learning potentials. Because of the efficient convergence of the atomic cluster expansion relevant subspaces can be sampled uniformly and exhaustively. This is demonstrated by testing against a large database of density functional theory calculations for copper.},
- keywords = {_tablet,ACE,descriptors,original publication},
+ keywords = {\_tablet,ACE,descriptors,original publication},
file = {/Users/wasmer/Nextcloud/Zotero/Drautz_2019_Atomic cluster expansion for accurate and transferable interatomic potentials.pdf;/Users/wasmer/Zotero/storage/HNR9ZCLL/Drautz_2019_Atomic cluster expansion for accurate and transferable interatomic potentials.pdf;/Users/wasmer/Zotero/storage/NMAUF3NJ/PhysRevB.99.html}
}
@@ -2376,7 +2860,7 @@
volume = {102},
number = {2},
doi = {10.1103/PhysRevB.102.024104},
- keywords = {_tablet,ACE,descriptors,magnetism,ML,spin-dependent},
+ keywords = {\_tablet,ACE,descriptors,magnetism,ML,spin-dependent},
file = {/Users/wasmer/Nextcloud/Zotero/Drautz_2020_Atomic cluster expansion of scalar, vectorial, and tensorial properties.pdf;/Users/wasmer/Zotero/storage/9W2WE4WX/PhysRevB.102.html}
}
@@ -2392,7 +2876,7 @@
urldate = {2022-06-28},
abstract = {The atomic cluster expansion (ACE) has been highly successful for the parameterisation of symmetric (invariant or equivariant) properties of many-particle systems. Here, we generalize its derivation to anti-symmetric functions. We show how numerous well-known linear representations of wave functions naturally arise within this framework and we explore how recent successful nonlinear parameterisations can be further enhanced by employing ACE methodology. From this analysis we propose a wide design space of promising wave function representations.},
pubstate = {preprint},
- keywords = {_tablet,ACE,Backflow,Deep learning,ML-QM,prediction of wavefunction,representation of wavefunction,Slater-Jastrow},
+ keywords = {\_tablet,ACE,Backflow,Deep learning,ML-QM,prediction of wavefunction,representation of wavefunction,Slater-Jastrow},
file = {/Users/wasmer/Nextcloud/Zotero/Drautz_Ortner_2022_Atomic cluster expansion and wave function representations.pdf;/Users/wasmer/Zotero/storage/6PTQT7NH/2206.html}
}
@@ -2459,7 +2943,7 @@
abstract = {Application to the Physics of Condensed Matter},
isbn = {978-3-540-32897-1},
langid = {english},
- keywords = {_tablet,condensed matter,group theory,irreps,learning material,mathematics,rec-by-sabastian,textbook},
+ keywords = {\_tablet,condensed matter,group theory,irreps,learning material,mathematics,rec-by-sabastian,textbook},
file = {/Users/wasmer/Nextcloud/Zotero/Dresselhaus et al_2007_Group Theory.pdf;/Users/wasmer/Zotero/storage/GGGVNLC4/978-3-540-32899-5.html}
}
@@ -2552,7 +3036,7 @@
urldate = {2021-10-04},
abstract = {The modern version of the KKR (Korringa–Kohn–Rostoker) method represents the electronic structure of a system directly and efficiently in terms of its single-particle Green's function (GF). This is in contrast to its original version and many other traditional wave-function-based all-electron band structure methods dealing with periodically ordered solids. Direct access to the GF results in several appealing features. In addition, a wide applicability of the method is achieved by employing multiple scattering theory. The basic ideas behind the resulting KKR-GF method are outlined and the different techniques to deal with the underlying multiple scattering problem are reviewed. Furthermore, various applications of the KKR-GF method are reviewed in some detail to demonstrate the remarkable flexibility of the approach. Special attention is devoted to the numerous developments of the KKR-GF method, that have been contributed in recent years by a number of work groups, in particular in the following fields: embedding schemes for atoms, clusters and surfaces, magnetic response functions and anisotropy, electronic and spin-dependent transport, dynamical mean field theory, various kinds of spectroscopies, as well as first-principles determination of model parameters.},
langid = {english},
- keywords = {_tablet,KKR,review},
+ keywords = {\_tablet,KKR,review},
file = {/Users/wasmer/Nextcloud/Zotero/Ebert et al_2011_Calculating condensed matter properties using the KKR-Green's function.pdf}
}
@@ -2584,7 +3068,7 @@
url = {https://link.aps.org/doi/10.1103/PhysRevB.104.035120},
urldate = {2021-12-05},
abstract = {We present a numerical modeling workflow based on machine learning which reproduces the total energies produced by Kohn-Sham density functional theory (DFT) at finite electronic temperature to within chemical accuracy at negligible computational cost. Based on deep neural networks, our workflow yields the local density of states (LDOS) for a given atomic configuration. From the LDOS, spatially resolved, energy-resolved, and integrated quantities can be calculated, including the DFT total free energy, which serves as the Born-Oppenheimer potential energy surface for the atoms. We demonstrate the efficacy of this approach for both solid and liquid metals and compare results between independent and unified machine-learning models for solid and liquid aluminum. Our machine-learning density functional theory framework opens up the path towards multiscale materials modeling for matter under ambient and extreme conditions at a computational scale and cost that is unattainable with current algorithms.},
- keywords = {DFT,finite-temperature DFT,LAMMPS,library,MALA,ML,ML-DFT,ML-ESM,prediction of LDOS,quantum,Quantum ESPRESSO,SNAP,with-code},
+ keywords = {DFT,finite-temperature DFT,LAMMPS,library,MALA,ML,ML-DFT,ML-ESM,prediction of electron density,prediction of LDOS,quantum,Quantum ESPRESSO,SNAP,VASP,with-code},
file = {/Users/wasmer/Nextcloud/Zotero/Ellis et al_2021_Accelerating finite-temperature Kohn-Sham density functional theory with deep.pdf;/Users/wasmer/Zotero/storage/AS2E35V9/PhysRevB.104.html}
}
@@ -2729,6 +3213,7 @@
urldate = {2023-02-15},
abstract = {We introduce a practical hybrid approach that combines orbital-free density functional theory (DFT) with Kohn-Sham DFT for speeding up first-principles molecular dynamics simulations. Equilibrated ionic configurations are generated using orbital-free DFT for subsequent Kohn-Sham DFT molecular dynamics. This leads to a massive reduction of the simulation time without any sacrifice in accuracy. We assess this finding across systems of different sizes and temperature, up to the warm dense matter regime. To that end, we use the cosine distance between the time series of radial distribution functions representing the ionic configurations. Likewise, we show that the equilibrated ionic configurations from this hybrid approach significantly enhance the accuracy of machine-learning models that replace Kohn-Sham DFT. Our hybrid scheme enables systematic first-principles simulations of warm dense matter that are otherwise hampered by the large numbers of atoms and the prevalent high temperatures. Moreover, our finding provides an additional motivation for developing kinetic and noninteracting free energy functionals for orbital-free DFT.},
pubstate = {preprint},
+ keywords = {AIMD,DFT,MD,OF-DFT},
file = {/Users/wasmer/Nextcloud/Zotero/Fiedler et al_2022_Accelerating Equilibration in First-Principles Molecular Dynamics with.pdf;/Users/wasmer/Zotero/storage/TA7XVJUP/2206.html}
}
@@ -2742,7 +3227,7 @@
url = {http://arxiv.org/abs/2110.00997},
urldate = {2021-11-17},
abstract = {With the growth of computational resources, the scope of electronic structure simulations has increased greatly. Artificial intelligence and robust data analysis hold the promise to accelerate large-scale simulations and their analysis to hitherto unattainable scales. Machine learning is a rapidly growing field for the processing of such complex datasets. It has recently gained traction in the domain of electronic structure simulations, where density functional theory takes the prominent role of the most widely used electronic structure method. Thus, DFT calculations represent one of the largest loads on academic high-performance computing systems across the world. Accelerating these with machine learning can reduce the resources required and enables simulations of larger systems. Hence, the combination of density functional theory and machine learning has the potential to rapidly advance electronic structure applications such as in-silico materials discovery and the search for new chemical reaction pathways. We provide the theoretical background of both density functional theory and machine learning on a generally accessible level. This serves as the basis of our comprehensive review including research articles up to December 2020 in chemistry and materials science that employ machine-learning techniques. In our analysis, we categorize the body of research into main threads and extract impactful results. We conclude our review with an outlook on exciting research directions in terms of a citation analysis.},
- keywords = {_tablet,citation analysis,DFT,literature analysis,ML,ML-DFT,ML-ESM,review},
+ keywords = {\_tablet,citation analysis,DFT,literature analysis,ML,ML-DFT,ML-ESM,review},
file = {/Users/wasmer/Nextcloud/Zotero/Fiedler et al_2021_A Deep Dive into Machine Learning Density Functional Theory for Materials.pdf;/Users/wasmer/Zotero/storage/2XW6IGEA/2110.html}
}
@@ -2758,7 +3243,7 @@
urldate = {2023-02-15},
abstract = {With the growth of computational resources, the scope of electronic structure simulations has increased greatly. Artificial intelligence and robust data analysis hold the promise to accelerate large-scale simulations and their analysis to hitherto unattainable scales. Machine learning is a rapidly growing field for the processing of such complex datasets. It has recently gained traction in the domain of electronic structure simulations, where density functional theory takes the prominent role of the most widely used electronic structure method. Thus, DFT calculations represent one of the largest loads on academic high-performance computing systems across the world. Accelerating these with machine learning can reduce the resources required and enables simulations of larger systems. Hence, the combination of density functional theory and machine learning has the potential to rapidly advance electronic structure applications such as in-silico materials discovery and the search for new chemical reaction pathways. We provide the theoretical background of both density functional theory and machine learning on a generally accessible level. This serves as the basis of our comprehensive review including research articles up to December 2020 in chemistry and materials science that employ machine-learning techniques. In our analysis, we categorize the body of research into main threads and extract impactful results. We conclude our review with an outlook on exciting research directions in terms of a citation analysis.},
pubstate = {preprint},
- keywords = {citation analysis,DFT,literature analysis,ML,ML-DFT,ML-ESM,review},
+ keywords = {citation analysis,DFT,literature analysis,ML,ML-DFA,ML-DFT,ML-ESM,prediction of electron density,review},
file = {/Users/wasmer/Nextcloud/Zotero/Fiedler et al_2022_A Deep Dive into Machine Learning Density Functional Theory for Materials.pdf;/Users/wasmer/Zotero/storage/NUWIJFTB/2110.html}
}
@@ -2776,10 +3261,26 @@
url = {https://link.aps.org/doi/10.1103/PhysRevMaterials.6.040301},
urldate = {2023-03-09},
abstract = {With the growth of computational resources, the scope of electronic structure simulations has increased greatly. Artificial intelligence and robust data analysis hold the promise to accelerate large-scale simulations and their analysis to hitherto unattainable scales. Machine learning is a rapidly growing field for the processing of such complex data sets. It has recently gained traction in the domain of electronic structure simulations, where density functional theory (DFT) takes the prominent role of the most widely used electronic structure method. Thus, DFT calculations represent one of the largest loads on academic high-performance computing systems across the world. Accelerating these with machine learning can reduce the resources required and enables simulations of larger systems. Hence, the combination of DFT and machine learning has the potential to rapidly advance electronic structure applications such as in silico materials discovery and the search for new chemical reaction pathways. We provide the theoretical background of both DFT and machine learning on a generally accessible level. This serves as the basis of our comprehensive review, including research articles up to December 2020 in chemistry and materials science that employ machine-learning techniques. In our analysis, we categorize the body of research into main threads and extract impactful results. We conclude our review with an outlook on exciting research directions in terms of a citation analysis.},
- keywords = {citation analysis,DFT,literature analysis,ML,ML-DFT,ML-ESM,review},
+ keywords = {citation analysis,DFT,literature analysis,ML,ML-DFA,ML-DFT,ML-ESM,prediction of electron density,review},
file = {/Users/wasmer/Nextcloud/Zotero/Fiedler et al_2022_Deep dive into machine learning density functional theory for materials science.pdf;/Users/wasmer/Zotero/storage/62FHUUPB/PhysRevMaterials.6.html}
}
+@online{fiedlerMachineLearningElectronic2023,
+ title = {Machine Learning the Electronic Structure of Matter across Temperatures},
+ author = {Fiedler, Lenz and Modine, Normand A. and Miller, Kyle D. and Cangi, Attila},
+ date = {2023-06-09},
+ eprint = {2306.06032},
+ eprinttype = {arxiv},
+ eprintclass = {cond-mat, physics:physics},
+ doi = {10.48550/arXiv.2306.06032},
+ url = {http://arxiv.org/abs/2306.06032},
+ urldate = {2023-06-13},
+ abstract = {We introduce machine learning (ML) models that predict the electronic structure of materials across a wide temperature range. Our models employ neural networks and are trained on density functional theory (DFT) data. Unlike other ML models that use DFT data, our models directly predict the local density of states (LDOS) of the electronic structure. This provides several advantages, including access to multiple observables such as the electronic density and electronic total free energy. Moreover, our models account for both the electronic and ionic temperatures independently, making them ideal for applications like laser-heating of matter. We validate the efficacy of our LDOS-based models on a metallic test system. They accurately capture energetic effects induced by variations in ionic and electronic temperatures over a broad temperature range, even when trained on a subset of these temperatures. These findings open up exciting opportunities for investigating the electronic structure of materials under both ambient and extreme conditions.},
+ pubstate = {preprint},
+ keywords = {aluminium,AML,DFT,finite-temperature DFT,library,MALA,Metals and alloys,ML,ML-DFT,ML-ESM,model reporting,PAW,PBE,prediction of electron density,prediction of LDOS,prediction of total energy,Quantum ESPRESSO,transfer learning,VASP,warm dense matter,with-code},
+ file = {/Users/wasmer/Nextcloud/Zotero/Fiedler et al_2023_Machine learning the electronic structure of matter across temperatures.pdf;/Users/wasmer/Zotero/storage/YX8Z9U2B/2306.html}
+}
+
@online{fiedlerPredictingElectronicStructures2022,
title = {Predicting Electronic Structures at Any Length Scale with Machine Learning},
author = {Fiedler, Lenz and Modine, Normand A. and Schmerler, Steve and Vogel, Dayton J. and Popoola, Gabriel A. and Thompson, Aidan P. and Rajamanickam, Sivasankaran and Cangi, Attila},
@@ -2792,9 +3293,31 @@
urldate = {2023-02-15},
abstract = {The properties of electrons in matter are of fundamental importance. They give rise to virtually all molecular and material properties and determine the physics at play in objects ranging from semiconductor devices to the interior of giant gas planets. Modeling and simulation of such diverse applications rely primarily on density functional theory (DFT), which has become the principal method for predicting the electronic structure of matter. While DFT calculations have proven to be very useful to the point of being recognized with a Nobel prize in 1998, their computational scaling limits them to small systems. We have developed a machine learning framework for predicting the electronic structure on any length scale. It shows up to three orders of magnitude speedup on systems where DFT is tractable and, more importantly, enables predictions on scales where DFT calculations are infeasible. Our work demonstrates how machine learning circumvents a long-standing computational bottleneck and advances science to frontiers intractable with any current solutions. This unprecedented modeling capability opens up an inexhaustible range of applications in astrophysics, novel materials discovery, and energy solutions for a sustainable future.},
pubstate = {preprint},
+ keywords = {DFT,finite-temperature DFT,LAMMPS,library,MALA,ML,ML-DFT,ML-ESM,prediction of electron density,prediction of LDOS,quantum,Quantum ESPRESSO,SNAP,VASP,with-code},
file = {/Users/wasmer/Nextcloud/Zotero/Fiedler et al_2022_Predicting electronic structures at any length scale with machine learning.pdf;/Users/wasmer/Zotero/storage/9AYDDQ8T/2210.html}
}
+@article{fiedlerPredictingElectronicStructures2023,
+ title = {Predicting Electronic Structures at Any Length Scale with Machine Learning},
+ author = {Fiedler, Lenz and Modine, Normand A. and Schmerler, Steve and Vogel, Dayton J. and Popoola, Gabriel A. and Thompson, Aidan P. and Rajamanickam, Sivasankaran and Cangi, Attila},
+ date = {2023-06-27},
+ journaltitle = {npj Computational Materials},
+ shortjournal = {npj Comput Mater},
+ volume = {9},
+ number = {1},
+ pages = {1--10},
+ publisher = {{Nature Publishing Group}},
+ issn = {2057-3960},
+ doi = {10.1038/s41524-023-01070-z},
+ url = {https://www.nature.com/articles/s41524-023-01070-z},
+ urldate = {2023-06-28},
+ abstract = {The properties of electrons in matter are of fundamental importance. They give rise to virtually all material properties and determine the physics at play in objects ranging from semiconductor devices to the interior of giant gas planets. Modeling and simulation of such diverse applications rely primarily on density functional theory (DFT), which has become the principal method for predicting the electronic structure of matter. While DFT calculations have proven to be very useful, their computational scaling limits them to small systems. We have developed a machine learning framework for predicting the electronic structure on any length scale. It shows up to three orders of magnitude speedup on systems where DFT is tractable and, more importantly, enables predictions on scales where DFT calculations are infeasible. Our work demonstrates how machine learning circumvents a long-standing computational bottleneck and advances materials science to frontiers intractable with any current solutions.},
+ issue = {1},
+ langid = {english},
+ keywords = {DFT,Electronic properties and materials,Electronic structure,finite-temperature DFT,LAMMPS,library,MALA,ML,ML-DFT,ML-ESM,prediction of electron density,prediction of LDOS,quantum,Quantum ESPRESSO,SNAP,VASP,with-code},
+ file = {/Users/wasmer/Nextcloud/Zotero/Fiedler et al_2023_Predicting electronic structures at any length scale with machine learning.pdf}
+}
+
@article{fiedlerTrainingfreeHyperparameterOptimization2022,
title = {Training-Free Hyperparameter Optimization of Neural Networks for Electronic Structures in Matter},
author = {Fiedler, Lenz and Hoffmann, Nils and Mohammed, Parvez and Popoola, Gabriel A. and Yovell, Tamar and Oles, Vladyslav and Ellis, J. Austin and Rajamanickam, Siva and Cangi, Attila},
@@ -2812,7 +3335,7 @@
url = {http://arxiv.org/abs/2202.09186},
urldate = {2023-02-15},
abstract = {A myriad of phenomena in materials science and chemistry rely on quantum-level simulations of the electronic structure in matter. While moving to larger length and time scales has been a pressing issue for decades, such large-scale electronic structure calculations are still challenging despite modern software approaches and advances in high-performance computing. The silver lining in this regard is the use of machine learning to accelerate electronic structure calculations -- this line of research has recently gained growing attention. The grand challenge therein is finding a suitable machine-learning model during a process called hyperparameter optimization. This, however, causes a massive computational overhead in addition to that of data generation. We accelerate the construction of neural network models by roughly two orders of magnitude by circumventing excessive training during the hyperparameter optimization phase. We demonstrate our workflow for Kohn-Sham density functional theory, the most popular computational method in materials science and chemistry.},
- keywords = {_tablet},
+ keywords = {\_tablet,AML,hyperparameters,hyperparameters optimization,MALA,ML,ML-DFT,Optuna,prediction of electron density,prediction of LDOS},
file = {/Users/wasmer/Nextcloud/Zotero/Fiedler et al_2022_Training-free hyperparameter optimization of neural networks for electronic.pdf;/Users/wasmer/Zotero/storage/6BKNM2VX/2202.html}
}
@@ -2845,25 +3368,46 @@
urldate = {2023-02-23},
abstract = {As the go-to method to solve the electronic structure problem, Kohn-Sham density functional theory (KS-DFT) can be used to obtain the ground-state charge density, total energy, and several other key materials' properties. Unfortunately, the solution of the Kohn-Sham equations is found iteratively. This is a numerically intensive task, limiting the possible size and complexity of the systems to be treated. Machine-learning (ML) models for the charge density can then be used as surrogates to generate the converged charge density and reduce the computational cost of solving the electronic structure problem. We derive a powerful grid-centred structural representation based on the Jacobi and Legendre polynomials that, combined with a linear regression built on a data-efficient workflow, can accurately learn the charge density. Then, we design a machine-learning pipeline that can return energy and forces at the quality of a converged DFT calculation but at a fraction of the computational cost. This can be used as a tool for the fast scanning of the energy landscape and as a starting point to the DFT self-consistent cycle, in both cases maintaining a low computational cost.},
pubstate = {preprint},
- keywords = {_tablet,Condensed Matter - Materials Science,prediction of electron density},
+ keywords = {\_tablet,AML,DFT,grid-based descriptors,Jacobi-Legendre potential,library,ML,ML-DFT,prediction of electron density,VASP,with-code},
file = {/Users/wasmer/Nextcloud/Zotero/Focassio et al_2023_Linear Jacobi-Legendre expansion of the charge density for machine.pdf;/Users/wasmer/Zotero/storage/HPSZ89R2/2301.html}
}
+@article{focassioLinearJacobiLegendreExpansion2023a,
+ title = {Linear {{Jacobi-Legendre}} Expansion of the Charge Density for Machine Learning-Accelerated Electronic Structure Calculations},
+ author = {Focassio, Bruno and Domina, Michelangelo and Patil, Urvesh and Fazzio, Adalberto and Sanvito, Stefano},
+ date = {2023-05-29},
+ journaltitle = {npj Computational Materials},
+ shortjournal = {npj Comput Mater},
+ volume = {9},
+ number = {1},
+ pages = {1--10},
+ publisher = {{Nature Publishing Group}},
+ issn = {2057-3960},
+ doi = {10.1038/s41524-023-01053-0},
+ url = {https://www.nature.com/articles/s41524-023-01053-0},
+ urldate = {2023-05-30},
+ abstract = {Kohn–Sham density functional theory (KS-DFT) is a powerful method to obtain key materials’ properties, but the iterative solution of the KS equations is a numerically intensive task, which limits its application to complex systems. To address this issue, machine learning (ML) models can be used as surrogates to find the ground-state charge density and reduce the computational overheads. We develop a grid-centred structural representation, based on Jacobi and Legendre polynomials combined with a linear regression, to accurately learn the converged DFT charge density. This integrates into a ML pipeline that can return any density-dependent observable, including energy and forces, at the quality of a converged DFT calculation, but at a fraction of the computational cost. Fast scanning of energy landscapes and producing starting densities for the DFT self-consistent cycle are among the applications of our scheme.},
+ issue = {1},
+ langid = {english},
+ keywords = {\_tablet,AML,Computational methods,DFT,Electronic structure,grid-based descriptors,Jacobi-Legendre potential,library,ML,ML-DFT,prediction of electron density,VASP,with-code},
+ file = {/Users/wasmer/Nextcloud/Zotero/Focassio et al_2023_Linear Jacobi-Legendre expansion of the charge density for machine2.pdf}
+}
+
@report{foulkesTopologyEntanglementStrong2020,
title = {Topology, {{Entanglement}}, and {{Strong Correlations}}},
author = {Foulkes, W. M. C. and Drautz, Ralf},
+ editorb = {Pavarini, Eva and Koch, Erik},
+ editorbtype = {redactor},
date = {2020},
number = {FZJ-2020-03083},
institution = {{Forschungszentrum Jülich GmbH Zentralbibliothek, Verlag}},
url = {https://juser.fz-juelich.de/record/884084/},
urldate = {2022-06-28},
abstract = {Topology and entanglement are key concepts in many-body physics. Understanding the as-sociated emergent phenomena beyond toy models – in the world of real strongly-correlatedmaterials – requires the mastery of a wealth of different methods. These encompass analytical tools such as group theory, first principles techniques based on density-functional theory, materials-specific model-building schemes, as well as advanced modern numerical approaches for solving realistic many-body models. This year’s school provides an overview of the state-of-the art of these methods, their successes and their limitations. After introducing the basics, lectures will present the core concepts of topology and entanglement in many-body systems. To make contact to real materials, strategies for building materials specific models and techniques for their solution will be introduced. Among the latter, the school will cover quantum Monte Carlo methods, construction and optimization of correlated wave-functions, recursion and renormalization group techniques, as well as dynamical mean-field theory. More advanced lectures will give a pedagogical overview ontopological materials and their physics: topological metals, semimetals, and superconductors. Towards the end of the school entanglement in quantum dynamics and perspectives in quantum computation will be discussed. The goal of the school is to introduce advanced graduate students and up to these modern approaches for the realistic modeling of strongly correlated materials. A school of this size and scope requires backing from many sources. This is even more truethis year. As everywhere, the Corona pandemics provided scores of new challenges. Plans had to be changed and real facilities had to be replaced with virtual ones. We are very grateful forall the practical and financial support we have received. The Institute for Advanced Simulationat the Forschungszentrum J ülich and the Jülich Supercomputer Centre provided the major part of the funding and were vital for the organization and reorganization of the school as well as for the production of this book. The Institute for Complex Adaptive Matter (ICAM) supplied additional funds and ideas for successful online formats. The nature of a school makes it desirable to have the lecture notes available when the lecturesare given. This way students get the chance to work through the lectures thoroughly while their memory is still fresh. We are therefore extremely grateful to the lecturers that, despite tight deadlines, provided their manuscripts in time for the production of this book. We are confident that the lecture notes collected here will not only serve the participants of the school but will also be useful for other students entering the exciting field of strongly correlated materials. We are grateful to Mrs. H. Lexis of the Verlag des Forschungszentrum Jülich and to Mrs. D. Mans of the Grafische Betriebe for providing their expert support in producing the present volume on a tight schedule. We heartily thank our students and postdocs who helped with proofreading the manuscripts, often on quite short notice: Elaheh Adibi, Julian Mußhoff, NedaSamani, and Xue-Jing Zhang. Finally, our special thanks go to Dipl.-Ing. R. Hölzle for his invaluable advice on the innu-merable questions concerning the organization of such an endeavor, and to Mrs. L. Snyders forexpertly handling all practical issues. Pavarini, Eva; Koch, Erik},
- editorb = {Pavarini, Eva and Koch, Erik},
- editorbtype = {redactor},
eventtitle = {Autumn {{School}} on {{Correlated Electrons}}},
isbn = {9783958064669},
langid = {english},
- keywords = {_tablet,ACE,cusps,FermiNet,ML-ESM,ML-QMBP,prediction of wavefunction,QMC,Slater-Jastrow,VMC},
+ keywords = {\_tablet,ACE,cusps,FermiNet,ML-ESM,ML-QMBP,prediction of wavefunction,QMC,Slater-Jastrow,VMC},
file = {/Users/wasmer/Nextcloud/Zotero/Foulkes_Drautz_2020_Topology, Entanglement, and Strong Correlations.pdf;/Users/wasmer/Zotero/storage/WLIE37SZ/884084.html}
}
@@ -2877,7 +3421,7 @@
doi = {10.1109/IJCNN55064.2022.9892358},
abstract = {Learning 3D representations of point clouds that generalize well to arbitrary orientations is a challenge of practical importance in domains ranging from computer vision to molecular modeling. The proposed approach uses a concentric spherical spatial representation, formed by nesting spheres discretized the icosahedral grid, as the basis for structured learning over point clouds. We propose rotationally equivariant convolutions for learning over the concentric spherical grid, which are incorporated into a novel architecture for representation learning that is robust to general rotations in 3D. We demonstrate the effectiveness and extensibility of our approach to problems in different domains, such as 3D shape recognition and predicting fundamental properties of molecular systems.},
eventtitle = {2022 {{International Joint Conference}} on {{Neural Networks}} ({{IJCNN}})},
- keywords = {AML,convolution,CSNN,equivariant,General ML,GNN,library,ML,ML-DFT,ML-ESM,NN,original publication,point cloud compression,point cloud data,prediction of DOS,representation learning,spherical convolution,spherical NN},
+ keywords = {\_tablet,AML,convolution,CSNN,equivariant,General ML,GNN,library,ML,ML-DFT,ML-ESM,NN,original publication,point cloud compression,point cloud data,prediction of DOS,representation learning,spherical convolution,spherical NN},
file = {/Users/wasmer/Nextcloud/Zotero/Fox et al_2022_Concentric Spherical Neural Network for 3D Representation Learning.pdf;/Users/wasmer/Zotero/storage/YBYXAFZ3/9892358.html}
}
@@ -2911,7 +3455,7 @@
urldate = {2023-04-04},
abstract = {The application of machine learning methods in quantum chemistry has enabled the study of numerous chemical phenomena, which are computationally intractable with traditional ab-initio methods. However, some quantum mechanical properties of molecules and materials depend on non-local electronic effects, which are often neglected due to the difficulty of modeling them efficiently. This work proposes a modified attention mechanism adapted to the underlying physics, which allows to recover the relevant non-local effects. Namely, we introduce spherical harmonic coordinates (SPHCs) to reflect higher-order geometric information for each atom in a molecule, enabling a non-local formulation of attention in the SPHC space. Our proposed model So3krates - a self-attention based message passing neural network - uncouples geometric information from atomic features, making them independently amenable to attention mechanisms. Thereby we construct spherical filters, which extend the concept of continuous filters in Euclidean space to SPHC space and serve as foundation for a spherical self-attention mechanism. We show that in contrast to other published methods, So3krates is able to describe non-local quantum mechanical effects over arbitrary length scales. Further, we find evidence that the inclusion of higher-order geometric correlations increases data efficiency and improves generalization. So3krates matches or exceeds state-of-the-art performance on popular benchmarks, notably, requiring a significantly lower number of parameters (0.25 - 0.4x) while at the same time giving a substantial speedup (6 - 14x for training and 2 - 11x for inference) compared to other models.},
pubstate = {preprint},
- keywords = {AML,attention,equivariant,Euclidean space,FLAX,GDL,invariance,JAX,library,long-range interaction,ML,ML-FF,molecules,MPNN,NequIP,original publication,QM7-X,SchNet,sGDML,SO(3),spherical harmonic coordinates,SpookyNet,with-code},
+ keywords = {\_tablet,AML,attention,equivariant,Euclidean space,FLAX,GDL,invariance,JAX,library,long-range interaction,ML,ML-FF,molecules,MPNN,NequIP,original publication,QM7-X,SchNet,sGDML,SO(3),spherical harmonic coordinates,SpookyNet,with-code},
file = {/Users/wasmer/Nextcloud/Zotero/Frank et al_2023_So3krates.pdf;/Users/wasmer/Zotero/storage/9GZJ7VNS/2205.html}
}
@@ -2930,7 +3474,7 @@
urldate = {2022-10-03},
abstract = {Fraux et al., (2020). Chemiscope: interactive structure-property explorer for materials and molecules. Journal of Open Source Software, 5(51), 2117, https://doi.org/10.21105/joss.02117},
langid = {english},
- keywords = {_tablet,data exploration,Database,ML,molecules,sketchmap,solids,unsupervised learning,visualization},
+ keywords = {\_tablet,data exploration,Database,ML,molecules,sketchmap,solids,unsupervised learning,visualization},
file = {/Users/wasmer/Nextcloud/Zotero/Fraux et al_2020_Chemiscope.pdf;/Users/wasmer/Zotero/storage/TCQI9XE2/joss.html}
}
@@ -2980,6 +3524,24 @@
file = {/Users/wasmer/Zotero/storage/26HPBYJN/RevModPhys.86.html}
}
+@article{freysoldtLimitationsEmpiricalSupercell2022,
+ title = {Limitations of Empirical Supercell Extrapolation for Calculations of Point Defects in Bulk, at Surfaces, and in Two-Dimensional Materials},
+ author = {Freysoldt, Christoph and Neugebauer, Jörg and Tan, Anne Marie Z. and Hennig, Richard G.},
+ date = {2022-01-07},
+ journaltitle = {Physical Review B},
+ shortjournal = {Phys. Rev. B},
+ volume = {105},
+ number = {1},
+ pages = {014103},
+ publisher = {{American Physical Society}},
+ doi = {10.1103/PhysRevB.105.014103},
+ url = {https://link.aps.org/doi/10.1103/PhysRevB.105.014103},
+ urldate = {2023-05-06},
+ abstract = {The commonly employed supercell approach for defects in crystalline materials may introduce spurious interactions between the defect and its periodic images. A rich literature is available on how the interaction energies can be estimated, reduced, or corrected. A simple and seemingly straightforward approach is to extrapolate from a series of finite supercell sizes to the infinite-size limit, assuming a smooth polynomial dependence of the energy on inverse supercell size. In this work, we demonstrate by means of explict density-functional theory supercell calculations and simplified models that wave-function overlap and electrostatic interactions lead to more complex dependencies on supercell size than commonly assumed. We show that this complexity cannot be captured by the simple extrapolation approaches and that suitable correction schemes should be employed.},
+ keywords = {\_tablet,/unread,2D material,defects,DFT,impurity embedding,interfaces and thin films,KKR,point defects,supercell,surface physics},
+ file = {/Users/wasmer/Nextcloud/Zotero/Freysoldt et al_2022_Limitations of empirical supercell extrapolation for calculations of point.pdf;/Users/wasmer/Zotero/storage/EAVNV5DB/PhysRevB.105.html}
+}
+
@article{friederichMachinelearnedPotentialsNextgeneration2021,
title = {Machine-Learned Potentials for next-Generation Matter Simulations},
author = {Friederich, Pascal and Häse, Florian and Proppe, Jonny and Aspuru-Guzik, Alán},
@@ -3070,7 +3632,7 @@
url = {https://link.aps.org/doi/10.1103/PhysRevMaterials.6.023802},
urldate = {2023-04-04},
abstract = {Automatic exhaustive exploration of a large material space by high-performance supercomputers is crucial for developing new functional materials. We demonstrated the efficiency of high-throughput calculations using the all-electron Korringa-Kohn-Rostoker coherent potential approximation method with the density functional theory for the large material space consisting of quaternary high entropy alloys, which are nonstoichiometric and substitutionally disordered materials. The exhaustive calculations were performed for 147 630 systems based on the AkaiKKR program package and supercomputer Fugaku, where the numerical parameters and self-consistent convergence are automatically controlled. The large material database including the total energies, magnetization, Curie temperature, and residual resistivity was constructed by our calculations. We used frequent itemset mining to identify the characteristics of parcels in magnetization and Curie temperature space. We also identified the elements that enhance the magnetization and Curie temperature and clarified the rough dependence of the elements through regression modeling of the residual resistivity.},
- keywords = {/unread,CPA,HTC,KKR},
+ keywords = {\_tablet,/unread,CPA,HTC,KKR},
file = {/Users/wasmer/Nextcloud/Zotero/Fukushima et al_2022_Automatic exhaustive calculations of large material space by.pdf;/Users/wasmer/Zotero/storage/VNUQ6LGT/PhysRevMaterials.6.html}
}
@@ -3118,6 +3680,21 @@
keywords = {OO,Reusability,software engineering,Software patterns}
}
+@online{gandhiExplainingMolecularProperties2022,
+ title = {Explaining Molecular Properties with Natural Language},
+ author = {Gandhi, Heta A. and White, Andrew D.},
+ date = {2022-10-03},
+ eprinttype = {ChemRxiv},
+ doi = {10.26434/chemrxiv-2022-v5p6m-v3},
+ url = {https://chemrxiv.org/engage/chemrxiv/article-details/633731d1f764e6e535093041},
+ urldate = {2023-05-20},
+ abstract = {We present a model-agnostic method that gives natural language explanations of molecular structure property predictions. Machine learning models are now common for molecular property prediction and chemical design. They typically are black boxes -- having no explanation for predictions. We show how to use surrogate models to attribute predictions to chemical descriptors and molecular substructures, independent of the black box model inputs. The method generates explanations consistent with chemical reasoning, like connecting existence of a functional group or molecular polarity. We see in a genuine test like blood brain barrier permeation, our descriptor explanations match biologically observed SARs with mechanistic support. We show these quantitative explanations can be further translated to natural language.},
+ langid = {english},
+ pubstate = {preprint},
+ keywords = {AML,chemistry,contrastive explanation,counterfactual explanation,Deep learning,descriptor explanation,descriptors,descriptors analysis,feature attribution,GPT,GPT-3,graph attribution,library,LIME,LLM,MACCS,ML,model explanation,nlp,property prediction,QSAR,SAR,SELFIES,SHAP,STONED,surrogate model,with-code,XAI},
+ file = {/Users/wasmer/Nextcloud/Zotero/Gandhi_White_2022_Explaining molecular properties with natural language.pdf}
+}
+
@article{gaoSelfconsistentDeterminationLongrange2022,
title = {Self-Consistent Determination of Long-Range Electrostatics in Neural Network Potentials},
author = {Gao, Ang and Remsing, Richard C.},
@@ -3135,26 +3712,10 @@
abstract = {Machine learning has the potential to revolutionize the field of molecular simulation through the development of efficient and accurate models of interatomic interactions. Neural networks can model interactions with the accuracy of quantum mechanics-based calculations, but with a fraction of the cost, enabling simulations of large systems over long timescales. However, implicit in the construction of neural network potentials is an assumption of locality, wherein atomic arrangements on the nanometer-scale are used to learn interatomic interactions. Because of this assumption, the resulting neural network models cannot describe long-range interactions that play critical roles in dielectric screening and chemical reactivity. Here, we address this issue by introducing the self-consistent field neural network — a general approach for learning the long-range response of molecular systems in neural network potentials that relies on a physically meaningful separation of the interatomic interactions — and demonstrate its utility by modeling liquid water with and without applied fields.},
issue = {1},
langid = {english},
- keywords = {/unread,AML,electrostatic interaction,long-range interaction,MD,ML,MLP,NN,SCF,SCFNN},
+ keywords = {AML,Electric field,electrostatic interaction,equivariant,long-range interaction,MD,ML,MLP,MLWFC,NN,prediction of electronic response,prediction of forces,SCF,SCFNN,Wannier},
file = {/Users/wasmer/Nextcloud/Zotero/Gao_Remsing_2022_Self-consistent determination of long-range electrostatics in neural network.pdf}
}
-@online{gardnerSyntheticDataEnable2022,
- title = {Synthetic Data Enable Experiments in Atomistic Machine Learning},
- author = {Gardner, John L. A. and Beaulieu, Zoé Faure and Deringer, Volker L.},
- date = {2022-11-29},
- eprint = {2211.16443},
- eprinttype = {arxiv},
- eprintclass = {physics},
- doi = {10.48550/arXiv.2211.16443},
- url = {http://arxiv.org/abs/2211.16443},
- urldate = {2022-12-29},
- abstract = {Machine-learning models are increasingly used to predict properties of atoms in chemical systems. There have been major advances in developing descriptors and regression frameworks for this task, typically starting from (relatively) small sets of quantum-mechanical reference data. Larger datasets of this kind are becoming available, but remain expensive to generate. Here we demonstrate the use of a large dataset that we have "synthetically" labelled with per-atom energies from an existing ML potential model. The cheapness of this process, compared to the quantum-mechanical ground truth, allows us to generate millions of datapoints, in turn enabling rapid experimentation with atomistic ML models from the small- to the large-data regime. This approach allows us here to compare regression frameworks in depth, and to explore visualisation based on learned representations. We also show that learning synthetic data labels can be a useful pre-training task for subsequent fine-tuning on small datasets. In the future, we expect that our open-sourced dataset, and similar ones, will be useful in rapidly exploring deep-learning models in the limit of abundant chemical data.},
- pubstate = {preprint},
- keywords = {_tablet,data augmentation,GAP,GPR,MD,ML,MLP,NN,prediction of potential energy,small data,SOAP,synthetic data},
- file = {/Users/wasmer/Nextcloud/Zotero/Gardner et al_2022_Synthetic data enable experiments in atomistic machine learning.pdf;/Users/wasmer/Zotero/storage/N3NP679J/2211.html}
-}
-
@online{gardnerSyntheticDataEnable2022a,
title = {Synthetic Data Enable Experiments in Atomistic Machine Learning},
author = {Gardner, John L. A. and Beaulieu, Zoé Faure and Deringer, Volker L.},
@@ -3167,7 +3728,7 @@
urldate = {2023-03-01},
abstract = {Machine-learning models are increasingly used to predict properties of atoms in chemical systems. There have been major advances in developing descriptors and regression frameworks for this task, typically starting from (relatively) small sets of quantum-mechanical reference data. Larger datasets of this kind are becoming available, but remain expensive to generate. Here we demonstrate the use of a large dataset that we have "synthetically" labelled with per-atom energies from an existing ML potential model. The cheapness of this process, compared to the quantum-mechanical ground truth, allows us to generate millions of datapoints, in turn enabling rapid experimentation with atomistic ML models from the small- to the large-data regime. This approach allows us here to compare regression frameworks in depth, and to explore visualisation based on learned representations. We also show that learning synthetic data labels can be a useful pre-training task for subsequent fine-tuning on small datasets. In the future, we expect that our open-sourced dataset, and similar ones, will be useful in rapidly exploring deep-learning models in the limit of abundant chemical data.},
pubstate = {preprint},
- keywords = {/unread,carbon,data augmentation,Database,disordered,DKL,GAP,GPR,KPCovR,LAMMPS,MD,MLP,NN,SOAP,Supervised learning,synthetic data,UMAP,unsupervised learning},
+ keywords = {/unread,carbon,data augmentation,Database,disordered,DKL,GAP,GPR,KPCovR,LAMMPS,MD,ML,MLP,NN,prediction of potential energy,small data,SOAP,Supervised learning,synthetic data,UMAP,unsupervised learning},
file = {/Users/wasmer/Nextcloud/Zotero/Gardner et al_2022_Synthetic data enable experiments in atomistic machine learning2.pdf;/Users/wasmer/Zotero/storage/99FPUBGW/2211.html}
}
@@ -3254,6 +3815,22 @@
file = {/Users/wasmer/Nextcloud/Zotero/Gasteiger et al_2022_GemNet.pdf;/Users/wasmer/Zotero/storage/FE5R77B9/2106.html}
}
+@online{gaviniRoadmapElectronicStructure2022,
+ title = {Roadmap on {{Electronic Structure Codes}} in the {{Exascale Era}}},
+ author = {Gavini, Vikram and Baroni, Stefano and Blum, Volker and Bowler, David R. and Buccheri, Alexander and Chelikowsky, James R. and Das, Sambit and Dawson, William and Delugas, Pietro and Dogan, Mehmet and Draxl, Claudia and Galli, Giulia and Genovese, Luigi and Giannozzi, Paolo and Giantomassi, Matteo and Gonze, Xavier and Govoni, Marco and Gulans, Andris and Gygi, François and Herbert, John M. and Kokott, Sebastian and Kühne, Thomas D. and Liou, Kai-Hsin and Miyazaki, Tsuyoshi and Motamarri, Phani and Nakata, Ayako and Pask, John E. and Plessl, Christian and Ratcliff, Laura E. and Richard, Ryan M. and Rossi, Mariana and Schade, Robert and Scheffler, Matthias and Schütt, Ole and Suryanarayana, Phanish and Torrent, Marc and Truflandier, Lionel and Windus, Theresa L. and Xu, Qimen and Yu, Victor W.-Z. and Perez, Danny},
+ date = {2022-09-26},
+ eprint = {2209.12747},
+ eprinttype = {arxiv},
+ eprintclass = {cond-mat, physics:physics},
+ doi = {10.48550/arXiv.2209.12747},
+ url = {http://arxiv.org/abs/2209.12747},
+ urldate = {2023-06-30},
+ abstract = {Electronic structure calculations have been instrumental in providing many important insights into a range of physical and chemical properties of various molecular and solid-state systems. Their importance to various fields, including materials science, chemical sciences, computational chemistry and device physics, is underscored by the large fraction of available public supercomputing resources devoted to these calculations. As we enter the exascale era, exciting new opportunities to increase simulation numbers, sizes, and accuracies present themselves. In order to realize these promises, the community of electronic structure software developers will however first have to tackle a number of challenges pertaining to the efficient use of new architectures that will rely heavily on massive parallelism and hardware accelerators. This roadmap provides a broad overview of the state-of-the-art in electronic structure calculations and of the various new directions being pursued by the community. It covers 14 electronic structure codes, presenting their current status, their development priorities over the next five years, and their plans towards tackling the challenges and leveraging the opportunities presented by the advent of exascale computing.},
+ pubstate = {preprint},
+ keywords = {ABINIT,BigDFT,chemistry,CONQUEST,CP2K,DFT,DFT-FE,ESM,exascale,FHI-aims,HPC,ML-DFT,NWChem,PARSEC,physics,Q-Chem,Quantum ESPRESSO,roadmap},
+ file = {/Users/wasmer/Nextcloud/Zotero/Gavini et al_2022_Roadmap on Electronic Structure Codes in the Exascale Era.pdf;/Users/wasmer/Zotero/storage/B2ZN8AD8/2209.html}
+}
+
@article{gebauerInverseDesign3d2022,
title = {Inverse Design of 3d Molecular Structures with Conditional Generative Neural Networks},
author = {Gebauer, Niklas W. A. and Gastegger, Michael and Hessmann, Stefaan S. P. and Müller, Klaus-Robert and Schütt, Kristof T.},
@@ -3307,10 +3884,23 @@
urldate = {2022-08-21},
abstract = {We present e3nn, a generalized framework for creating E(3) equivariant trainable functions, also known as Euclidean neural networks. e3nn naturally operates on geometry and geometric tensors that describe systems in 3D and transform predictably under a change of coordinate system. The core of e3nn are equivariant operations such as the TensorProduct class or the spherical harmonics functions that can be composed to create more complex modules such as convolutions and attention mechanisms. These core operations of e3nn can be used to efficiently articulate Tensor Field Networks, 3D Steerable CNNs, Clebsch-Gordan Networks, SE(3) Transformers and other E(3) equivariant networks.},
pubstate = {preprint},
- keywords = {_tablet,e3nn,EGNN,ENN,equivariant,library,ML-ESM,prediction of electron density,with-code},
+ keywords = {\_tablet,e3nn,EGNN,ENN,equivariant,library,ML-ESM,prediction of electron density,with-code},
file = {/Users/wasmer/Nextcloud/Zotero/Geiger_Smidt_2022_e3nn.pdf;/Users/wasmer/Zotero/storage/SJW8392C/2207.html}
}
+@online{gelzinyteWflPythonToolkit2023,
+ title = {Wfl {{Python Toolkit}} for {{Creating Machine Learning Interatomic Potentials}} and {{Related Atomistic Simulation Workflows}}},
+ author = {Gelžinytė, Elena and Wengert, Simon and Stenczel, Tamás K. and Heenen, Hendrik H. and Reuter, Karsten and Csányi, Gábor and Bernstein, Noam},
+ date = {2023-06-20},
+ url = {https://arxiv.org/abs/2306.11421v1},
+ urldate = {2023-07-01},
+ abstract = {Predictive atomistic simulations are increasingly employed for data intensive high throughput studies that take advantage of constantly growing computational resources. To handle the sheer number of individual calculations that are needed in such studies, workflow management packages for atomistic simulations have been developed for a rapidly growing user base. These packages are predominantly designed to handle computationally heavy ab initio calculations, usually with a focus on data provenance and reproducibility. However, in related simulation communities, e.g. the developers of machine learning interatomic potentials (MLIPs), the computational requirements are somewhat different: the types, sizes, and numbers of computational tasks are more diverse, and therefore require additional ways of parallelization and local or remote execution for optimal efficiency. In this work, we present the atomistic simulation and MLIP fitting workflow management package wfl and Python remote execution package ExPyRe to meet these requirements. With wfl and ExPyRe, versatile Atomic Simulation Environment based workflows that perform diverse procedures can be written. This capability is based on a low-level developer-oriented framework, which can be utilized to construct high level functionality for user-friendly programs. Such high level capabilities to automate machine learning interatomic potential fitting procedures are already incorporated in wfl, which we use to showcase its capabilities in this work. We believe that wfl fills an important niche in several growing simulation communities and will aid the development of efficient custom computational tasks.},
+ langid = {english},
+ organization = {{arXiv.org}},
+ keywords = {/unread},
+ file = {/Users/wasmer/Nextcloud/Zotero/Gelžinytė et al_2023_wfl Python Toolkit for Creating Machine Learning Interatomic Potentials and.pdf}
+}
+
@article{genschComprehensiveDiscoveryPlatform2021,
title = {A {{Comprehensive Discovery Platform}} for {{Organophosphorus Ligands}} for {{Catalysis}}},
author = {Gensch, Tobias and family=Passos Gomes, given=Gabriel, prefix=dos, useprefix=true and Friederich, Pascal and Peters, Ellyn and Gaudin, Theophile and Pollice, Robert and Jorner, Kjell and Nigam, AkshatKumar and Lindner D'Addario, Michael and Sigman, Matthew S. and Aspuru-Guzik, Alan},
@@ -3372,7 +3962,7 @@
url = {https://link.aps.org/doi/10.1103/PhysRevB.92.045131},
urldate = {2023-04-04},
abstract = {Based on an analysis of the short-range chemical environment of each atom in a system, standard machine-learning-based approaches to the construction of interatomic potentials aim at determining directly the central quantity, which is the total energy. This prevents, for instance, an accurate description of the energetics of systems in which long-range charge transfer or ionization is important. We propose therefore not to target directly with machine-learning methods the total energy but an intermediate physical quantity, namely, the charge density, which then in turn allows us to determine the total energy. By allowing the electronic charge to distribute itself in an optimal way over the system, we can describe not only neutral but also ionized systems with unprecedented accuracy. We demonstrate the power of our approach for both neutral and ionized NaCl clusters where charge redistribution plays a decisive role for the energetics. We are able to obtain chemical accuracy, i.e., errors of less than a millihartree per atom compared to the reference density functional results for a huge data set of configurations with large structural variety. The introduction of physically motivated quantities which are determined by the short-range atomic environment via a neural network also leads to an increased stability of the machine-learning process and transferability of the potential.},
- keywords = {AML,BigDFT,charge equilibration,charge transfer,electronegativity,electrostatic interaction,ionic systems,LDA,long-range interaction,ML,ML-DFT,MLP,NN,NNP,PES,prediction of electron density,prediction of electronegativity},
+ keywords = {\_tablet,AML,BigDFT,charge equilibration,charge transfer,electronegativity,electrostatic interaction,ionic systems,LDA,long-range interaction,ML,ML-DFT,MLP,NN,NNP,PES,prediction of electron density,prediction of electronegativity},
file = {/Users/wasmer/Nextcloud/Zotero/Ghasemi et al_2015_Interatomic potentials for ionic systems with density functional accuracy based.pdf;/Users/wasmer/Zotero/storage/HYUBBIPY/PhysRevB.92.html}
}
@@ -3448,7 +4038,7 @@
urldate = {2022-10-28},
abstract = {In this article we demonstrate the applications of classical and quantum machine learning in quantum transport and spintronics. With the help of a two terminal device with magnetic impurity we show how machine learning algorithms can predict the highly non-linear nature of conductance as well as the non-equilibrium spin response function for any random magnetic configuration. We finally describe the applicability of quantum machine learning which has the capability to handle a significantly large configuration space. Our approach is also applicable for molecular systems. These outcomes are crucial in predicting the behaviour of large scale systems where a quantum mechanical calculation is computationally challenging and therefore would play a crucial role in designing nano devices.},
pubstate = {preprint},
- keywords = {_tablet,ML,PGI-1/IAS-1,QML,QSVM,quantum computing,quantum transport,random forest,rec-by-ghosh,spin dynamics,Spintronics,SVM,tight binding,transport properties},
+ keywords = {\_tablet,ML,PGI-1/IAS-1,QML,QSVM,quantum computing,quantum transport,random forest,rec-by-ghosh,spin dynamics,Spintronics,SVM,tight binding,transport properties},
file = {/Users/wasmer/Nextcloud/Zotero/Ghosh_Ghosh_2022_Classical and quantum machine learning applications in spintronics.pdf;/Users/wasmer/Zotero/storage/FEUD8XZQ/2207.html}
}
@@ -3470,6 +4060,27 @@
file = {/Users/wasmer/Nextcloud/Zotero/Ghosh et al_2022_Short-range order and phase stability of CrCoNi explored with machine learning.pdf;/Users/wasmer/Zotero/storage/YXSSR3DU/PhysRevMaterials.6.html}
}
+@article{gibsonDataaugmentationGraphNeural2022,
+ title = {Data-Augmentation for Graph Neural Network Learning of the Relaxed Energies of Unrelaxed Structures},
+ author = {Gibson, Jason and Hire, Ajinkya and Hennig, Richard G.},
+ date = {2022-09-30},
+ journaltitle = {npj Computational Materials},
+ shortjournal = {npj Comput Mater},
+ volume = {8},
+ number = {1},
+ pages = {1--7},
+ publisher = {{Nature Publishing Group}},
+ issn = {2057-3960},
+ doi = {10.1038/s41524-022-00891-8},
+ url = {https://www.nature.com/articles/s41524-022-00891-8},
+ urldate = {2023-05-06},
+ abstract = {Computational materials discovery has grown in utility over the past decade due to advances in computing power and crystal structure prediction algorithms (CSPA). However, the computational cost of the ab initio calculations required by CSPA limits its utility to small unit cells, reducing the compositional and structural space the algorithms can explore. Past studies have bypassed unneeded ab initio calculations by utilizing machine learning to predict the stability of a material. Specifically, graph neural networks trained on large datasets of relaxed structures display high fidelity in predicting formation energy. Unfortunately, the geometries of structures produced by CSPA deviate from the relaxed state, which leads to poor predictions, hindering the model’s ability to filter unstable material. To remedy this behavior, we propose a simple, physically motivated, computationally efficient perturbation technique that augments training data, improving predictions on unrelaxed structures by 66\%. Finally, we show how this error reduction can accelerate CSPA.},
+ issue = {1},
+ langid = {english},
+ keywords = {/unread,AML,CGNN,crystal structure prediction,data augmentation,DFT,GNN,ML,prediction of structure,structure relaxation},
+ file = {/Users/wasmer/Nextcloud/Zotero/Gibson et al_2022_Data-augmentation for graph neural network learning of the relaxed energies of.pdf}
+}
+
@online{gilliganRulefreeWorkflowAutomated2023,
title = {A Rule-Free Workflow for the Automated Generation of Databases from Scientific Literature},
author = {Gilligan, Luke P. J. and Cobelli, Matteo and Taufour, Valentin and Sanvito, Stefano},
@@ -3482,7 +4093,7 @@
urldate = {2023-02-23},
abstract = {In recent times, transformer networks have achieved state-of-the-art performance in a wide range of natural language processing tasks. Here we present a workflow based on the fine-tuning of BERT models for different downstream tasks, which results in the automated extraction of structured information from unstructured natural language in scientific literature. Contrary to other methods for the automated extraction of structured compound-property relations from similar sources, our workflow does not rely on the definition of intricate grammar rules. Hence, it can be adapted to a new task without requiring extensive implementation efforts and knowledge. We test the data extraction performance by automatically generating a database of compounds and their associated Curie temperatures. This is compared with a manually curated database and one obtained with the state-of-the-art rule-based method. Finally, in order to demonstrate that the automatically extracted database can be used in a material-design workflow, we employ it to construct a machine-learning model predicting the Curie temperature based on a compound's chemical composition. This is quantitatively tested and compared with the best model constructed on manually-extracted data.},
pubstate = {preprint},
- keywords = {_tablet,BERT,data mining,database generation,literature analysis,LLM,materials},
+ keywords = {\_tablet,BERT,data mining,database generation,literature analysis,LLM,materials},
file = {/Users/wasmer/Nextcloud/Zotero/Gilligan et al_2023_A rule-free workflow for the automated generation of databases from scientific.pdf;/Users/wasmer/Zotero/storage/W8WDMBDK/2301.html}
}
@@ -3513,7 +4124,7 @@
abstract = {Modern Condensed Matter Physics brings together the most important advances in the field of recent decades. It provides instructors teaching graduate-level condensed matter courses with a comprehensive and in-depth textbook that will prepare graduate students for research or further study as well as reading more advanced and specialized books and research literature in the field. This textbook covers the basics of crystalline solids as well as analogous optical lattices and photonic crystals, while discussing cutting-edge topics such as disordered systems, mesoscopic systems, many-body systems, quantum magnetism, Bose–Einstein condensates, quantum entanglement, and superconducting quantum bits. Students are provided with the appropriate mathematical background to understand the topological concepts that have been permeating the field, together with numerous physical examples ranging from the fractional quantum Hall effect to topological insulators, the toric code, and majorana fermions. Exercises, commentary boxes, and appendices afford guidance and feedback for beginners and experts alike.},
isbn = {978-1-316-48064-9},
langid = {english},
- keywords = {_tablet,condensed matter,graduate,magnetism,superconductor,textbook,topological insulator},
+ keywords = {\_tablet,condensed matter,graduate,magnetism,superconductor,textbook,topological insulator},
file = {/Users/wasmer/Nextcloud/Zotero/Girvin_Yang_2019_Modern Condensed Matter Physics.pdf;/Users/wasmer/Zotero/storage/3FP65JQ3/F0A27AC5DEA8A40EA6EA5D727ED8B14E.html}
}
@@ -3607,6 +4218,27 @@
file = {/Users/wasmer/Nextcloud/Zotero/Golze et al_2019_The GW Compendium.pdf}
}
+@article{gongGeneralFrameworkEquivariant2023,
+ title = {General Framework for {{E}}(3)-Equivariant Neural Network Representation of Density Functional Theory {{Hamiltonian}}},
+ author = {Gong, Xiaoxun and Li, He and Zou, Nianlong and Xu, Runzhang and Duan, Wenhui and Xu, Yong},
+ date = {2023-05-18},
+ journaltitle = {Nature Communications},
+ shortjournal = {Nat Commun},
+ volume = {14},
+ number = {1},
+ pages = {2848},
+ publisher = {{Nature Publishing Group}},
+ issn = {2041-1723},
+ doi = {10.1038/s41467-023-38468-8},
+ url = {https://www.nature.com/articles/s41467-023-38468-8},
+ urldate = {2023-06-12},
+ abstract = {The combination of deep learning and ab initio calculation has shown great promise in revolutionizing future scientific research, but how to design neural network models incorporating a priori knowledge and symmetry requirements is a key challenging subject. Here we propose an E(3)-equivariant deep-learning framework to represent density functional theory (DFT) Hamiltonian as a function of material structure, which can naturally preserve the Euclidean symmetry even in the presence of spin–orbit coupling. Our DeepH-E3 method enables efficient electronic structure calculation at ab initio accuracy by learning from DFT data of small-sized structures, making the routine study of large-scale supercells ({$>$}104 atoms) feasible. The method can reach sub-meV prediction accuracy at high training efficiency, showing state-of-the-art performance in our experiments. The work is not only of general significance to deep-learning method development but also creates opportunities for materials research, such as building a Moiré-twisted material database.},
+ issue = {1},
+ langid = {english},
+ keywords = {\_tablet,AML,bismuth selenide,bismuth telluride,DeepH,E(3),e3nn,ENN,equivariant,library,magnetism,materials,ML,ML-DFT,ML-ESM,non-collinear,PAW,PBE,prediction of Hamiltonian matrix,PyTorch,SOC,spin-dependent,topological insulator,twisted bilayer graphene,VASP,vdW materials,with-code,with-data},
+ file = {/Users/wasmer/Nextcloud/Zotero/Gong et al_2023_General framework for E(3)-equivariant neural network representation of density.pdf;/Users/wasmer/Nextcloud/Zotero/Gong et al_2023_General framework for E(3)-equivariant neural network representation of density2.pdf;/Users/wasmer/Nextcloud/Zotero/Gong et al_2023_General framework for E(3)-equivariant neural network representation of density3.pdf;/Users/wasmer/Nextcloud/Zotero/Gong et al_2023_General framework for E(3)-equivariant neural network representation of density4.pdf}
+}
+
@unpublished{goodallRapidDiscoveryStable2022,
title = {Rapid {{Discovery}} of {{Stable Materials}} by {{Coordinate-free Coarse Graining}}},
author = {Goodall, Rhys E. A. and Parackal, Abhijith S. and Faber, Felix A. and Armiento, Rickard and Lee, Alpha A.},
@@ -3721,7 +4353,7 @@
urldate = {2022-07-02},
abstract = {The electron density of a molecule or material has recently received major attention as a target quantity of machine-learning models. A natural choice to construct a model that yields transferable and linear-scaling predictions is to represent the scalar field using a multi-centered atomic basis analogous to that routinely used in density fitting approximations. However, the non-orthogonality of the basis poses challenges for the learning exercise, as it requires accounting for all the atomic density components at once. We devise a gradient-based approach to directly minimize the loss function of the regression problem in an optimized and highly sparse feature space. In so doing, we overcome the limitations associated with adopting an atom-centered model to learn the electron density over arbitrarily complex datasets, obtaining extremely accurate predictions. The enhanced framework is tested on 32-molecule periodic cells of liquid water, presenting enough complexity to require an optimal balance between accuracy and computational efficiency. We show that starting from the predicted density a single Kohn-Sham diagonalization step can be performed to access total energy components that carry an error of just 0.1 meV/atom with respect to the reference density functional calculations. Finally, we test our method on the highly heterogeneous QM9 benchmark dataset, showing that a small fraction of the training data is enough to derive ground-state total energies within chemical accuracy.},
pubstate = {preprint},
- keywords = {_tablet,DFT,dimensionality reduction,equivariant,lambda-SOAP,ML-DFT,ML-ESM,molecules,molecules & solids,prediction of electron density,QM9,RKHS,SA-GPR,SALTED,SOAP,solids},
+ keywords = {\_tablet,DFT,dimensionality reduction,equivariant,lambda-SOAP,ML-DFT,ML-ESM,molecules,molecules \& solids,prediction of electron density,QM9,RKHS,SA-GPR,SALTED,SOAP,solids},
file = {/Users/wasmer/Nextcloud/Zotero/Grisafi et al_2022_Electronic-structure properties from atom-centered predictions of the electron.pdf;/Users/wasmer/Zotero/storage/QPHBS33I/2206.html}
}
@@ -3737,7 +4369,7 @@
url = {https://doi.org/10.1021/acs.jctc.2c00850},
urldate = {2023-01-25},
abstract = {The electron density of a molecule or material has recently received major attention as a target quantity of machine-learning models. A natural choice to construct a model that yields transferable and linear-scaling predictions is to represent the scalar field using a multicentered atomic basis analogous to that routinely used in density fitting approximations. However, the nonorthogonality of the basis poses challenges for the learning exercise, as it requires accounting for all the atomic density components at once. We devise a gradient-based approach to directly minimize the loss function of the regression problem in an optimized and highly sparse feature space. In so doing, we overcome the limitations associated with adopting an atom-centered model to learn the electron density over arbitrarily complex data sets, obtaining very accurate predictions using a comparatively small training set. The enhanced framework is tested on 32-molecule periodic cells of liquid water, presenting enough complexity to require an optimal balance between accuracy and computational efficiency. We show that starting from the predicted density a single Kohn–Sham diagonalization step can be performed to access total energy components that carry an error of just 0.1 meV/atom with respect to the reference density functional calculations. Finally, we test our method on the highly heterogeneous QM9 benchmark data set, showing that a small fraction of the training data is enough to derive ground-state total energies within chemical accuracy.},
- keywords = {DFT,dimensionality reduction,equivariant,lambda-SOAP,ML-DFT,ML-ESM,molecules,molecules & solids,prediction of electron density,QM9,RKHS,SA-GPR,SALTED,SOAP,solids},
+ keywords = {DFT,dimensionality reduction,equivariant,lambda-SOAP,ML-DFT,ML-ESM,molecules,molecules \& solids,prediction of electron density,QM9,RKHS,SA-GPR,SALTED,SOAP,solids},
file = {/Users/wasmer/Nextcloud/Zotero/Grisafi et al_2022_Electronic-Structure Properties from Atom-Centered Predictions of the Electron.pdf;/Users/wasmer/Zotero/storage/29HAHUDS/acs.jctc.html}
}
@@ -3799,6 +4431,22 @@
file = {/Users/wasmer/Nextcloud/Zotero/Grisafi et al_2021_Multi-scale approach for the prediction of atomic scale properties.pdf;/Users/wasmer/Zotero/storage/A6YSFTUU/Grisafi et al. - 2021 - Multi-scale approach for the prediction of atomic .pdf;/Users/wasmer/Zotero/storage/SG2EBGXV/d0sc04934d.html}
}
+@online{grisafiPredictingChargeDensity2023,
+ title = {Predicting the {{Charge Density Response}} in {{Metal Electrodes}}},
+ author = {Grisafi, Andrea and Bussy, Augustin and Vuilleumier, Rodolphe},
+ date = {2023-04-18},
+ eprint = {2304.08966},
+ eprinttype = {arxiv},
+ eprintclass = {cond-mat},
+ doi = {10.48550/arXiv.2304.08966},
+ url = {http://arxiv.org/abs/2304.08966},
+ urldate = {2023-05-26},
+ abstract = {The computational study of energy storage and conversion processes call for simulation techniques that can reproduce the electronic response of metal electrodes under electric fields. Despite recent advancements in machine-learning methods applied to electronic-structure properties, predicting the non-local behaviour of the charge density in electronic conductors remains a major open challenge. We combine long-range and equivariant kernel methods to predict the Kohn-Sham electron density of metal electrodes decomposed on an atom-centered basis. By taking slabs of gold as an example, we show that including long-range correlations into the learning model is essential to accurately reproduce the charge density and potential in bare electrodes of increasing size. A finite-field extension of the method is then introduced, which allows us to predict the charge transfer and the electrostatic potential drop induced by the application of an external electric field. Finally, we demonstrate the capability of the method to extrapolate the non-local electronic polarization generated by the interaction with an ionic species for electrodes of arbitrary thickness. Our study represents an important step forward in the accurate simulation of energy materials that include metallic interfaces.},
+ pubstate = {preprint},
+ keywords = {AML,DFT,electrochemistry,electrode,equivariant,GPR,kernel methods,LODE,long-range interaction,ML,ML-DFT,ML-ESM,prediction of electron density,SALTED,SOAP},
+ file = {/Users/wasmer/Nextcloud/Zotero/Grisafi et al_2023_Predicting the Charge Density Response in Metal Electrodes.pdf;/Users/wasmer/Zotero/storage/ZDZULVEX/2304.html}
+}
+
@article{grisafiSymmetryAdaptedMachineLearning2018,
title = {Symmetry-{{Adapted Machine Learning}} for {{Tensorial Properties}} of {{Atomistic Systems}}},
author = {Grisafi, Andrea and Wilkins, David M. and Csányi, Gábor and Ceriotti, Michele},
@@ -3832,7 +4480,7 @@
url = {https://doi.org/10.1021/acscentsci.8b00551},
urldate = {2021-10-19},
abstract = {The electronic charge density plays a central role in determining the behavior of matter at the atomic scale, but its computational evaluation requires demanding electronic-structure calculations. We introduce an atom-centered, symmetry-adapted framework to machine-learn the valence charge density based on a small number of reference calculations. The model is highly transferable, meaning it can be trained on electronic-structure data of small molecules and used to predict the charge density of larger compounds with low, linear-scaling cost. Applications are shown for various hydrocarbon molecules of increasing complexity and flexibility, and demonstrate the accuracy of the model when predicting the density on octane and octatetraene after training exclusively on butane and butadiene. This transferable, data-driven model can be used to interpret experiments, accelerate electronic structure calculations, and compute electrostatic interactions in molecules and condensed-phase systems.},
- keywords = {_tablet,DFT,GPR,lambda-SOAP,library,ML,ML-DFT,ML-ESM,models,molecules,prediction of electron density,SA-GPR,SALTED,SOAP,with-code},
+ keywords = {\_tablet,DFT,GPR,lambda-SOAP,library,ML,ML-DFT,ML-ESM,models,molecules,prediction of electron density,SA-GPR,SALTED,SOAP,with-code},
file = {/Users/wasmer/Nextcloud/Zotero/Grisafi et al_2019_Transferable Machine-Learning Model of the Electron Density.pdf;/Users/wasmer/Zotero/storage/HMBCGARZ/acscentsci.html}
}
@@ -3977,6 +4625,25 @@
file = {/Users/wasmer/Nextcloud/Zotero/Hartmaier_2020_Data-Oriented Constitutive Modeling of Plasticity in Metals.pdf;/Users/wasmer/Zotero/storage/94LTSN79/htm.html}
}
+@article{hasanColloquiumTopologicalInsulators2010,
+ title = {Colloquium: {{Topological}} Insulators},
+ shorttitle = {Colloquium},
+ author = {Hasan, M. Z. and Kane, C. L.},
+ date = {2010-11-08},
+ journaltitle = {Reviews of Modern Physics},
+ shortjournal = {Rev. Mod. Phys.},
+ volume = {82},
+ number = {4},
+ pages = {3045--3067},
+ publisher = {{American Physical Society}},
+ doi = {10.1103/RevModPhys.82.3045},
+ url = {https://link.aps.org/doi/10.1103/RevModPhys.82.3045},
+ urldate = {2023-06-15},
+ abstract = {Topological insulators are electronic materials that have a bulk band gap like an ordinary insulator but have protected conducting states on their edge or surface. These states are possible due to the combination of spin-orbit interactions and time-reversal symmetry. The two-dimensional (2D) topological insulator is a quantum spin Hall insulator, which is a close cousin of the integer quantum Hall state. A three-dimensional (3D) topological insulator supports novel spin-polarized 2D Dirac fermions on its surface. In this Colloquium the theoretical foundation for topological insulators and superconductors is reviewed and recent experiments are described in which the signatures of topological insulators have been observed. Transport experiments on HgTe∕CdTe quantum wells are described that demonstrate the existence of the edge states predicted for the quantum spin Hall insulator. Experiments on Bi1−xSbx, Bi2Se3, Bi2Te3, and Sb2Te3 are then discussed that establish these materials as 3D topological insulators and directly probe the topology of their surface states. Exotic states are described that can occur at the surface of a 3D topological insulator due to an induced energy gap. A magnetic gap leads to a novel quantum Hall state that gives rise to a topological magnetoelectric effect. A superconducting energy gap leads to a state that supports Majorana fermions and may provide a new venue for realizing proposals for topological quantum computation. Prospects for observing these exotic states are also discussed, as well as other potential device applications of topological insulators.},
+ keywords = {\_tablet},
+ file = {/Users/wasmer/Nextcloud/Zotero/Hasan_Kane_2010_Colloquium.pdf;/Users/wasmer/Zotero/storage/RXPD79NW/RevModPhys.82.html}
+}
+
@article{hasnipDensityFunctionalTheory2014,
title = {Density Functional Theory in the Solid State},
author = {Hasnip, Philip J. and Refson, Keith and Probert, Matt I. J. and Yates, Jonathan R. and Clark, Stewart J. and Pickard, Chris J.},
@@ -3990,12 +4657,33 @@
url = {https://royalsocietypublishing.org/doi/10.1098/rsta.2013.0270},
urldate = {2022-05-18},
abstract = {Density functional theory (DFT) has been used in many fields of the physical sciences, but none so successfully as in the solid state. From its origins in condensed matter physics, it has expanded into materials science, high-pressure physics and mineralogy, solid-state chemistry and more, powering entire computational subdisciplines. Modern DFT simulation codes can calculate a vast range of structural, chemical, optical, spectroscopic, elastic, vibrational and thermodynamic phenomena. The ability to predict structure–property relationships has revolutionized experimental fields, such as vibrational and solid-state NMR spectroscopy, where it is the primary method to analyse and interpret experimental spectra. In semiconductor physics, great progress has been made in the electronic structure of bulk and defect states despite the severe challenges presented by the description of excited states. Studies are no longer restricted to known crystallographic structures. DFT is increasingly used as an exploratory tool for materials discovery and computational experiments, culminating in ex nihilo crystal structure prediction, which addresses the long-standing difficult problem of how to predict crystal structure polymorphs from nothing but a specified chemical composition. We present an overview of the capabilities of solid-state DFT simulations in all of these topics, illustrated with recent examples using the CASTEP computer program.},
- keywords = {_tablet,condensed matter,DFT,review},
+ keywords = {\_tablet,condensed matter,DFT,review},
file = {/Users/wasmer/Nextcloud/Zotero/Hasnip et al_2014_Density functional theory in the solid state.pdf}
}
-@article{helfrechtStructurepropertyMapsKernel2020,
- title = {Structure-Property Maps with {{Kernel}} Principal Covariates Regression},
+@article{hegdeMachinelearnedApproximationsDensity2017,
+ title = {Machine-Learned Approximations to {{Density Functional Theory Hamiltonians}}},
+ author = {Hegde, Ganesh and Bowen, R. Chris},
+ date = {2017-02-15},
+ journaltitle = {Scientific Reports},
+ shortjournal = {Sci Rep},
+ volume = {7},
+ number = {1},
+ pages = {42669},
+ publisher = {{Nature Publishing Group}},
+ issn = {2045-2322},
+ doi = {10.1038/srep42669},
+ url = {https://www.nature.com/articles/srep42669},
+ urldate = {2023-06-30},
+ abstract = {Large scale Density Functional Theory (DFT) based electronic structure calculations are highly time consuming and scale poorly with system size. While semi-empirical approximations to DFT result in a reduction in computational time versus ab initio DFT, creating such approximations involves significant manual intervention and is highly inefficient for high-throughput electronic structure screening calculations. In this letter, we propose the use of machine-learning for prediction of DFT Hamiltonians. Using suitable representations of atomic neighborhoods and Kernel Ridge Regression, we show that an accurate and transferable prediction of DFT Hamiltonians for a variety of material environments can be achieved. Electronic structure properties such as ballistic transmission and band structure computed using predicted Hamiltonians compare accurately with their DFT counterparts. The method is independent of the specifics of the DFT basis or material system used and can easily be automated and scaled for predicting Hamiltonians of any material system of interest.},
+ issue = {1},
+ langid = {english},
+ keywords = {AML,bispectrum,carbon,copper,descriptors,DFT,KRR,materials,ML,ML-DFT,ML-ESM,prediction of Hamiltonian matrix,real-space Hamiltonian,TB},
+ file = {/Users/wasmer/Nextcloud/Zotero/Hegde_Bowen_2017_Machine-learned approximations to Density Functional Theory Hamiltonians.pdf}
+}
+
+@article{helfrechtStructurepropertyMapsKernel2020,
+ title = {Structure-Property Maps with {{Kernel}} Principal Covariates Regression},
author = {Helfrecht, Benjamin A. and Cersonsky, Rose K. and Fraux, Guillaume and Ceriotti, Michele},
date = {2020-11},
journaltitle = {Mach. Learn.: Sci. Technol.},
@@ -4009,12 +4697,12 @@
urldate = {2021-10-19},
abstract = {Data analyses based on linear methods constitute the simplest, most robust, and transparent approaches to the automatic processing of large amounts of data for building supervised or unsupervised machine learning models. Principal covariates regression (PCovR) is an underappreciated method that interpolates between principal component analysis and linear regression and can be used conveniently to reveal structure-property relations in terms of simple-to-interpret, low-dimensional maps. Here we provide a pedagogic overview of these data analysis schemes, including the use of the kernel trick to introduce an element of non-linearity while maintaining most of the convenience and the simplicity of linear approaches. We then introduce a kernelized version of PCovR and a sparsified extension, and demonstrate the performance of this approach in revealing and predicting structure-property relations in chemistry and materials science, showing a variety of examples including elemental carbon, porous silicate frameworks, organic molecules, amino acid conformers, and molecular materials.},
langid = {english},
- keywords = {_tablet,KPCovR,KRR,models,original publication,PCovR,regression,Supervised learning,unsupervised learning},
+ keywords = {\_tablet,KPCovR,KRR,models,original publication,PCovR,regression,Supervised learning,unsupervised learning},
file = {/Users/wasmer/Nextcloud/Zotero/Helfrecht et al_2020_Structure-property maps with Kernel principal covariates regression.pdf}
}
@article{henkTopologicalCharacterMagnetism2012,
- title = {Topological {{Character}} and {{Magnetism}} of the {{Dirac State}} in {{Mn-Doped}} \$\{\textbackslash mathrm\{\vphantom{\}\}}{{Bi}}\vphantom\{\}\vphantom\{\}\_\{2\}\{\textbackslash mathrm\{\vphantom{\}\}}{{Te}}\vphantom\{\}\vphantom\{\}\_\{3\}\$},
+ title = {Topological {{Character}} and {{Magnetism}} of the {{Dirac State}} in {{Mn-Doped Bi2Te3}}},
author = {Henk, J. and Flieger, M. and Maznichenko, I. V. and Mertig, I. and Ernst, A. and Eremeev, S. V. and Chulkov, E. V.},
date = {2012-08-16},
journaltitle = {Physical Review Letters},
@@ -4027,7 +4715,76 @@
url = {https://link.aps.org/doi/10.1103/PhysRevLett.109.076801},
urldate = {2022-05-13},
abstract = {First-principles and model calculations show that the Dirac surface state of the topological insulator Bi2Te3 survives upon moderate Mn doping of the surface layers but can lose its topological character as a function of magnetization direction. The dispersion depends considerably on the direction of the Mn magnetization: for perpendicular magnetization, a gap of 16 meV opens up at the Dirac point; for in-plane magnetization, a tiny gap can be opened or closed in dependence on the magnetization azimuth. The ground state is ferromagnetic, with a critical temperature of 12 K. The results provide a path towards a magnetic control of the topological character of the Dirac surface state and its consequences to spin-dependent transport properties.},
- file = {/Users/wasmer/Nextcloud/Zotero/Henk et al_2012_Topological Character and Magnetism of the Dirac State in Mn-Doped.pdf;/Users/wasmer/Zotero/storage/W6BV33VI/Henk et al. - 2012 - Topological Character and Magnetism of the Dirac S.pdf;/Users/wasmer/Zotero/storage/ZTFJBVIM/PhysRevLett.109.html}
+ keywords = {\_tablet,AFM,Bi2Te3,bismuth telluride,Heisenberg model,Jij,KKR,magnetic doping,Mn doping,rec-by-ruess,topological insulator},
+ file = {/Users/wasmer/Nextcloud/Zotero/Henk et al_2012_Topological Character and Magnetism of the Dirac State in Mn-Doped Bi2Te3.pdf;/Users/wasmer/Zotero/storage/W6BV33VI/Henk et al_2012_Topological Character and Magnetism of the Dirac State in Mn-Doped Bi2Te3.pdf;/Users/wasmer/Zotero/storage/ZTFJBVIM/PhysRevLett.109.html}
+}
+
+@inproceedings{hennigBenchmarkingOptimizationUF3,
+ title = {Benchmarking and {{Optimization}} of {{UF}}{\textsuperscript{3}} {{Machine Learning Potential}} on {{Solids}}},
+ booktitle = {Bulletin of the {{American Physical Society}}},
+ author = {Hennig, Richard G.},
+ publisher = {{American Physical Society}},
+ url = {https://meetings.aps.org/Meeting/MAR23/Session/N53.7},
+ urldate = {2023-05-06},
+ eventtitle = {{{APS March Meeting}} 2023},
+ keywords = {/unread,AML,benchmarking,descriptor comparison,GAP,hyperparameters optimization,ML,MLP,MLP comparison,MTP,NNP,qSNAP,Ray Tune,SNAP,UF3},
+ file = {/Users/wasmer/Zotero/storage/IPQ8XQGV/N53.html}
+}
+
+@inproceedings{hennigInvestigationTransitionMetal,
+ title = {Investigation of Transition Metal Complex Representations for Machine Learning Structure-Property Relationships},
+ booktitle = {Bulletin of the {{American Physical Society}}},
+ author = {Hennig, Richard G.},
+ publisher = {{American Physical Society}},
+ url = {https://meetings.aps.org/Meeting/MAR23/Session/T00.315},
+ urldate = {2023-05-06},
+ abstract = {Molecular magnets have potential applications in quantum computing, spintronics, and sensor development. These molecules display spin anisotropy below their characteristic blocking temperature. Contenders for single molecular magnets are monometallic transition metal complexes. Modeling of these complexes demand high computational cost and is difficult due to strong coupling effects. We investigate the performance of crystal graph neural networks (CGNN) for the prediction of properties using a dataset containing nearly 87,000 transition metal complexes. These properties have been calculated using the TPSSh-D3BJ exchange-correlation functional. Here, we see if the CGNN can predict the HOMO/LUMO gap, metal ion charge, and a variety of other computed energies. We then compare the model performance of the CGNN against neural networks trained with structural descriptor representations, such as the smooth overlap of atomic positions (SOAP). A completed model can be used to filter complexes in a high throughput screening. This work provides the first steps in the development of a machine-learning model for the property prediction of transition metal complexes for single molecular magnet applications. *This work is funded by the DOE},
+ eventtitle = {{{APS March Meeting}} 2023},
+ keywords = {/unread,AML,CGNN,conference contribution,DFT,GNN,magnetism,ML,molecular magnet,poster,prediction of HOMO/LUMO,prediction of ion charge,SOAP,transition metal complex,transition metals},
+ file = {/Users/wasmer/Zotero/storage/HXVSPBUK/T00.html}
+}
+
+@inproceedings{hennigMachineLearningMonte,
+ title = {Machine Learning and {{Monte Carlo}} Simulations of the {{Gibbs}} Free Energy of the {{Fe-C}} System in a Magnetic Field},
+ booktitle = {Bulletin of the {{American Physical Society}}},
+ author = {Hennig, Richard G.},
+ publisher = {{American Physical Society}},
+ url = {https://meetings.aps.org/Meeting/MAR23/Session/D44.2},
+ urldate = {2023-05-06},
+ abstract = {To model the thermodynamics and kinetics of steels in high magnetic fields requires knowledge of the magnetic Gibbs free energy, G, which involves millions of energy evaluations for the potential energy landscapes as a function of the applied field. Density-functional theory (DFT) calculations provide sufficient accuracy but are computationally very demanding. To overcome this barrier, we apply the ultra-fast force field (UF3) machine learning model [1] to approximate the DFT energy landscape. A DFT database is assembled through VASP, focusing on the energies and forces as a function of magnetic field for bcc and fcc Fe(C) with different structural and magnetic configurations. The UF3 models are trained and validated on this database to quickly evaluate the energies of ensembles. The resulting UF3 models are then utilized in the subsequent Monte Carlo simulations. Thermodynamic integration is utilized to combine the simulations at different temperatures to achieve the magnetic G models for the two Fe(C) phases as a function of temperature, atomic fraction of carbon, and magnetic field. Our calculations show that the applied magnetic field of around 10 T results in a change in the transition temperature of tens of kelvins. [1] S. R. Xie et al, arXiv:2110.00624 (2021).},
+ eventtitle = {{{APS March Meeting}} 2023},
+ keywords = {/unread,AML,conference contribution,DFT,Gibbs free energy,iron,magnetic field,magnetism,MC,ML,MLP,mol,poster,prediction from magnetic field,prediction of free energy,UF3,VASP},
+ file = {/Users/wasmer/Zotero/storage/UTVWTFWN/D44.html}
+}
+
+@article{herbstBlackboxInhomogeneousPreconditioning2021,
+ title = {Black-Box Inhomogeneous Preconditioning for Self-Consistent Field Iterations in Density Functional Theory},
+ author = {Herbst, Michael F. and Levitt, Antoine},
+ date = {2021-02-24},
+ journaltitle = {Journal of Physics: Condensed Matter},
+ shortjournal = {J. Phys.: Condens. Matter},
+ volume = {33},
+ number = {8},
+ eprint = {2009.01665},
+ eprinttype = {arxiv},
+ eprintclass = {cond-mat, physics:physics},
+ pages = {085503},
+ issn = {0953-8984, 1361-648X},
+ doi = {10.1088/1361-648X/abcbdb},
+ url = {http://arxiv.org/abs/2009.01665},
+ urldate = {2023-05-26},
+ abstract = {We propose a new preconditioner based on the local density of states for computing the self-consistent problem in Kohn-Sham density functional theory. This preconditioner is inexpensive and able to cure the long-range charge sloshing known to hamper convergence in large, inhomogeneous systems such as clusters and surfaces. It is based on a parameter-free and physically motivated approximation to the independent-particle susceptibility operator, appropriate for both metals and insulators. It can be extended to semiconductors by using the macroscopic electronic dielectric constant as a parameter in the model. We test our preconditioner successfully on inhomogeneous systems containing metals, insulators, semiconductors and vacuum.},
+ keywords = {/unread,DFT,KS-DFT,numerical analysis,preconditioner,SCF},
+ file = {/Users/wasmer/Nextcloud/Zotero/Herbst_Levitt_2021_Black-box inhomogeneous preconditioning for self-consistent field iterations in.pdf;/Users/wasmer/Zotero/storage/66YFJEHI/2009.html}
+}
+
+@online{herbstCECAMErrorControl,
+ title = {{{CECAM}} - {{Error}} Control in First-Principles {{modellingError}} Control in First-Principles Modelling},
+ author = {Herbst, Michael F. and Csányi, Gábor and Dusson, Genevieve and Marzouk, Youssef},
+ url = {https://www.cecam.org/workshop-details/1115},
+ urldate = {2023-05-31},
+ keywords = {/unread,active learning,AML,Bayesian methods,DFT,error estimate,event,ML,MLP,uncertainty quantification,workshop},
+ file = {/Users/wasmer/Zotero/storage/XAIZ2E53/1115.html}
}
@article{herbstDFTKJulianApproach2021,
@@ -4038,9 +4795,71 @@
journaltitle = {JuliaCon Proceedings},
doi = {10.21105/JCON.00069},
abstract = {Density-functional theory (DFT) is a widespread method for simulating the quantum-chemical behaviour of electrons in matter. It provides a first-principles description of many optical, mechanical and chemical properties at an acceptable computational cost [16, 2, 3]. For a wide range of systems the obtained predictions are accurate and shortcomings of the theory are by now wellunderstood [2, 3]. The desire to tackle even bigger systems and more involved materials, however, keeps posing novel challenges that require methods to constantly improve. One example are socalled high-throughput screening approaches, which are becoming prominent in recent years. In these techniques one wishes to systematically scan over huge design spaces of compounds in order to identify promising novel materials for targeted follow-up investigation. This has already lead to many success stories [14], such as the discovery of novel earth-abundant semiconductors [11], novel light-absorbing materials [20], electrocatalysts [8], materials for hydrogen storage [13] or for Li-ion batteries [1]. Keeping in mind the large range of physics that needs to be covered in these studies as well as the typical number of calculations (up to the order of millions), a bottleneck in these studies is the reliability and performance of the underlying DFT codes.},
+ keywords = {DFT,Julia,library,numerical analysis,software,with-code},
file = {/Users/wasmer/Nextcloud/Zotero/Herbst et al_2021_DFTK.pdf}
}
+@article{herbstPosterioriErrorEstimation2020,
+ title = {A Posteriori Error Estimation for the Non-Self-Consistent {{Kohn-Sham}} Equations},
+ author = {Herbst, Michael F. and Levitt, Antoine and Cancès, Eric},
+ date = {2020},
+ journaltitle = {Faraday Discussions},
+ shortjournal = {Faraday Discuss.},
+ volume = {224},
+ eprint = {2004.13549},
+ eprinttype = {arxiv},
+ eprintclass = {cond-mat, physics:physics},
+ pages = {227--246},
+ issn = {1359-6640, 1364-5498},
+ doi = {10.1039/D0FD00048E},
+ url = {http://arxiv.org/abs/2004.13549},
+ urldate = {2023-05-26},
+ abstract = {We address the problem of bounding rigorously the errors in the numerical solution of the Kohn-Sham equations due to (i) the finiteness of the basis set, (ii) the convergence thresholds in iterative procedures, (iii) the propagation of rounding errors in floating-point arithmetic. In this contribution, we compute fully-guaranteed bounds on the solution of the non-self-consistent equations in the pseudopotential approximation in a plane-wave basis set. We demonstrate our methodology by providing band structure diagrams of silicon annotated with error bars indicating the combined error.},
+ keywords = {/unread,a posteriori,DFT,error estimate,KS equations,KS-DFT,N-SCF,numerical analysis},
+ file = {/Users/wasmer/Nextcloud/Zotero/Herbst et al_2020_A posteriori error estimation for the non-self-consistent Kohn-Sham equations.pdf;/Users/wasmer/Zotero/storage/TBV2QR7C/2004.html}
+}
+
+@article{herbstQuantifyingErrorCorevalence2020,
+ title = {Quantifying the Error of the Core-Valence Separation Approximation},
+ author = {Herbst, Michael F. and Fransson, Thomas},
+ date = {2020-08-07},
+ journaltitle = {The Journal of Chemical Physics},
+ shortjournal = {J. Chem. Phys.},
+ volume = {153},
+ number = {5},
+ eprint = {2005.05848},
+ eprinttype = {arxiv},
+ eprintclass = {physics},
+ pages = {054114},
+ issn = {0021-9606, 1089-7690},
+ doi = {10.1063/5.0013538},
+ url = {http://arxiv.org/abs/2005.05848},
+ urldate = {2023-05-26},
+ abstract = {For the calculation of core-excited states probed through X-ray absorption spectroscopy, the core-valence separation (CVS) scheme has become a vital tool. This approach allows to target such states with high specificity, albeit introducing an error. We report the implementation of a post-processing step for CVS excitations obtained within the algebraic-diagrammatic construction scheme for the polarisation propagator (ADC), which removes this error. Based on this we provide a detailed analysis of the CVS scheme, identifying its accuracy to be dominated by an error balance between two neglected couplings, one between core and valence single excitations and one between single and double core excitations. The selection of the basis set is shown to be vital for a proper description of both couplings, with tight polarising functions being necessary for a good balance of errors. The CVS error is confirmed to be stable across multiple systems, with an element-specific spread for \$K\$-edge spectrum calculations of about \$\textbackslash pm\$0.02 eV. A systematic lowering of the CVS error by 0.02-0.03 eV is noted when considering excitations to extremely diffuse states, emulating ionisation.},
+ keywords = {/unread,core electrons,DFT,error estimate,KS-DFT,numerical analysis,numerical errors,valence electrons},
+ file = {/Users/wasmer/Nextcloud/Zotero/Herbst_Fransson_2020_Quantifying the error of the core-valence separation approximation.pdf;/Users/wasmer/Zotero/storage/HDEA9XMC/2005.html}
+}
+
+@article{herbstRobustEfficientLine2022,
+ title = {A Robust and Efficient Line Search for Self-Consistent Field Iterations},
+ author = {Herbst, Michael F. and Levitt, Antoine},
+ date = {2022-06},
+ journaltitle = {Journal of Computational Physics},
+ shortjournal = {Journal of Computational Physics},
+ volume = {459},
+ eprint = {2109.14018},
+ eprinttype = {arxiv},
+ eprintclass = {cond-mat, physics:physics},
+ pages = {111127},
+ issn = {00219991},
+ doi = {10.1016/j.jcp.2022.111127},
+ url = {http://arxiv.org/abs/2109.14018},
+ urldate = {2023-05-26},
+ abstract = {We propose a novel adaptive damping algorithm for the self-consistent field (SCF) iterations of Kohn-Sham density-functional theory, using a backtracking line search to automatically adjust the damping in each SCF step. This line search is based on a theoretically sound, accurate and inexpensive model for the energy as a function of the damping parameter. In contrast to usual damped SCF schemes, the resulting algorithm is fully automatic and does not require the user to select a damping. We successfully apply it to a wide range of challenging systems, including elongated supercells, surfaces and transition-metal alloys.},
+ keywords = {/unread,DFT,iterative algorithms,line search,mathematics,numerical analysis,SCF},
+ file = {/Users/wasmer/Nextcloud/Zotero/Herbst_Levitt_2022_A robust and efficient line search for self-consistent field iterations.pdf;/Users/wasmer/Zotero/storage/4VSZA7RL/2109.html}
+}
+
@unpublished{herbstSurrogateModelsQuantum2021,
title = {Surrogate Models for Quantum Spin Systems Based on Reduced Order Modeling},
author = {Herbst, Michael F. and Wessel, Stefan and Rizzi, Matteo and Stamm, Benjamin},
@@ -4051,7 +4870,7 @@
url = {http://arxiv.org/abs/2110.15665},
urldate = {2022-01-11},
abstract = {We present a methodology to investigate phase-diagrams of quantum models based on the principle of the reduced basis method (RBM). The RBM is built from a few ground-state snapshots, i.e., lowest eigenvectors of the full system Hamiltonian computed at well-chosen points in the parameter space of interest. We put forward a greedy-strategy to assemble such small-dimensional basis, i.e., to select where to spend the numerical effort needed for the snapshots. Once the RBM is assembled, physical observables required for mapping out the phase-diagram (e.g., structure factors) can be computed for any parameter value with a modest computational complexity, considerably lower than the one associated to the underlying Hilbert space dimension. We benchmark the method in two test cases, a chain of excited Rydberg atoms and a geometrically frustrated antiferromagnetic two-dimensional lattice model, and illustrate the accuracy of the approach. In particular, we find that the ground-manifold can be approximated to sufficient accuracy with a moderate number of basis functions, which increases very mildly when the number of microscopic constituents grows - in stark contrast to the exponential growth of the Hilbert space needed to describe each of the few snapshots. A combination of the presented RBM approach with other numerical techniques circumventing even the latter big cost, e.g., Tensor Network methods, is a tantalising outlook of this work.},
- keywords = {Condensed Matter - Strongly Correlated Electrons,Quantum Physics},
+ keywords = {alternative to ML,DFT,model order reduction,numerical analysis,quantum spin,spin,surrogate model},
file = {/Users/wasmer/Nextcloud/Zotero/Herbst et al_2021_Surrogate models for quantum spin systems based on reduced order modeling.pdf;/Users/wasmer/Zotero/storage/F4FW6AHT/2110.html}
}
@@ -4094,6 +4913,26 @@
file = {/Users/wasmer/Nextcloud/Zotero/Herr et al_2019_Compressing physics with an autoencoder.pdf}
}
+@article{heTopologicalInsulatorSpintronics2019,
+ title = {Topological Insulator: {{Spintronics}} and Quantum Computations},
+ shorttitle = {Topological Insulator},
+ author = {He, Mengyun and Sun, Huimin and He, Qing Lin},
+ date = {2019-05-03},
+ journaltitle = {Frontiers of Physics},
+ shortjournal = {Front. Phys.},
+ volume = {14},
+ number = {4},
+ pages = {43401},
+ issn = {2095-0470},
+ doi = {10.1007/s11467-019-0893-4},
+ url = {https://doi.org/10.1007/s11467-019-0893-4},
+ urldate = {2023-06-14},
+ abstract = {Topological insulators are emergent states of quantum matter that are gapped in the bulk with time-reversal symmetry-preserved gapless edge/surface states, adiabatically distinct from conventional materials. By proximity to various magnets and superconductors, topological insulators show novel physics at the interfaces, which give rise to two new areas named topological spintronics and topological quantum computation. Effects in the former such as the spin torques, spin-charge conversion, topological antiferromagnetic spintronics, and skyrmions realized in topological systems will be addressed. In the latter, a superconducting pairing gap leads to a state that supports Majorana fermions states, which may provide a new path for realizing topological quantum computation. Various signatures of Majorana zero modes/edge mode in topological superconductors will be discussed. The review ends by outlooks and potential applications of topological insulators. Topological superconductors that are fabricated using topological insulators with superconductors have a full pairing gap in the bulk and gapless surface states consisting of Majorana fermions. The theory of topological superconductors is reviewed, in close analogy to the theory of topological insulators.},
+ langid = {english},
+ keywords = {\_tablet,applications,ARPES,Hall effect,Hall QAHE,Majorana,quantum computing,review,skyrmions,Spintronics,topological insulator,Topological Superconductor},
+ file = {/Users/wasmer/Nextcloud/Zotero/He et al_2019_Topological insulator.pdf}
+}
+
@article{himanenDScribeLibraryDescriptors2020,
title = {{{DScribe}}: {{Library}} of Descriptors for Machine Learning in Materials Science},
shorttitle = {{{DScribe}}},
@@ -4148,6 +4987,35 @@
file = {/Users/wasmer/Nextcloud/Zotero/Hirohata et al_2020_Review on spintronics.pdf}
}
+@online{hodappEquivariantTensorNetworks2023,
+ title = {Equivariant {{Tensor Networks}}},
+ author = {Hodapp, Max and Shapeev, Alexander},
+ date = {2023-04-17},
+ eprint = {2304.08226},
+ eprinttype = {arxiv},
+ eprintclass = {physics},
+ doi = {10.48550/arXiv.2304.08226},
+ url = {http://arxiv.org/abs/2304.08226},
+ urldate = {2023-05-26},
+ abstract = {Machine-learning interatomic potentials (MLIPs) have made a significant contribution to the recent progress in the fields of computational materials and chemistry due to MLIPs' ability of accurately approximating energy landscapes of quantum-mechanical models while being orders of magnitude more computationally efficient. However, the computational cost and number of parameters of many state-of-the-art MLIPs increases exponentially with the number of atomic features. Tensor (non-neural) networks, based on low-rank representations of high-dimensional tensors, have been a way to reduce the number of parameters in approximating multidimensional functions, however, it is often not easy to encode the model symmetries into them. In this work we develop a formalism for rank-efficient equivariant tensor networks (ETNs), i.e., tensor networks that remain invariant under actions of SO(3) upon contraction. All the key algorithms of tensor networks like orthogonalization of cores and DMRG carry over to our equivariant case. Moreover, we show that many elements of modern neural network architectures like message passing, pulling, or attention mechanisms, can in some form be implemented into the ETNs. Based on ETNs, we develop a new class of polynomial-based MLIPs that demonstrate superior performance over existing MLIPs for multicomponent systems.},
+ pubstate = {preprint},
+ keywords = {ACE,AML,binary systems,dimensionality reduction,DMRG,equivariant,HEA,ML,MLP,MTP,multi-component systems,QM7,QM9,quaternary systems,SO(3),tensor network},
+ file = {/Users/wasmer/Nextcloud/Zotero/Hodapp_Shapeev_2023_Equivariant Tensor Networks.pdf;/Users/wasmer/Zotero/storage/J2HAIBJ3/2304.html}
+}
+
+@software{hoffmannMapleScriptsCalculation2019,
+ title = {Maple Scripts for the Calculation of {{Hubbard}} Matrices and Their Subsequent Downfolding by {{Loewdin}}'s Partitioning},
+ author = {Hoffmann, Markus and Ohs, Nicholas and Blügel, Stefan},
+ date = {2019-12-23},
+ doi = {10.5281/zenodo.3609779},
+ url = {https://zenodo.org/record/3609779},
+ urldate = {2023-04-27},
+ abstract = {We provide here small Maple scripts for the calculation of Hubbard matrices and their subsequent downfolding by Loewdin's partitioning},
+ organization = {{Zenodo}},
+ keywords = {/unread,downfolding,Hubbard model,Loewdin's partitioning},
+ file = {/Users/wasmer/Zotero/storage/62QDRCHY/3609779.html}
+}
+
@article{hohenbergInhomogeneousElectronGas1964,
title = {Inhomogeneous {{Electron Gas}}},
author = {Hohenberg, P.},
@@ -4231,6 +5099,81 @@
file = {/Users/wasmer/Nextcloud/Zotero/Hong et al_2021_Reducing Time to Discovery.pdf}
}
+@article{honraoAugmentingMachineLearning2020,
+ title = {Augmenting Machine Learning of Energy Landscapes with Local Structural Information},
+ author = {Honrao, Shreyas J. and Xie, Stephen R. and Hennig, Richard G.},
+ date = {2020-08-26},
+ journaltitle = {Journal of Applied Physics},
+ shortjournal = {Journal of Applied Physics},
+ volume = {128},
+ number = {8},
+ pages = {085101},
+ issn = {0021-8979},
+ doi = {10.1063/5.0012407},
+ url = {https://doi.org/10.1063/5.0012407},
+ urldate = {2023-05-06},
+ abstract = {We present a machine learning approach for accurately predicting formation energies of binary compounds in the context of crystal structure predictions. The success of any machine learning model depends significantly on the choice of representation used to encode the relevant physical information into machine-learnable data. We test different representation schemes based on partial radial and angular distribution functions (RDF+ADF) on Al–Ni and Cd–Te structures generated using our genetic algorithm for structure prediction. We observe a remarkable improvement in predictive accuracy upon transitioning from global to atom-centered representations, resulting in a threefold decrease in prediction errors. We show that a support vector regression model using a combination of atomic radial and angular distribution functions performs best at the formation energy prediction task, providing small root mean squared errors of 3.9\,meV/atom and 10.9\,meV/atom for Al–Ni and Cd–Te, respectively. We test the performance of our models against common traditional descriptors and find that RDF- and ADF-based representations significantly outperform many of those in the prediction of formation energies. The high accuracy of predictions makes our machine learning models great candidates for the exploration of energy landscapes.},
+ keywords = {\_tablet,/unread,ACDC,ACSF,ADF descriptor,AML,benchmarking,binary systems,CFID,Coulomb matrix,crystal structure prediction,descriptor comparison,descriptors,LAMMPS,MBTR,MD,ML,OFM descriptor,PES,prediction of energy,prediction of formation energy,RDF descriptor,SB descriptors,SOAP,structure prediction},
+ file = {/Users/wasmer/Nextcloud/Zotero/Honrao et al_2020_Augmenting machine learning of energy landscapes with local structural.pdf;/Users/wasmer/Zotero/storage/VFKSDW8H/Augmenting-machine-learning-of-energy-landscapes.html}
+}
+
+@article{huAisNetUniversalInteratomic2023,
+ title = {{{AisNet}}: {{A Universal Interatomic Potential Neural Network}} with {{Encoded Local Environment Features}}},
+ shorttitle = {{{AisNet}}},
+ author = {Hu, Zheyu and Guo, Yaolin and Liu, Zhen and Shi, Diwei and Li, Yifan and Hu, Yu and Bu, Moran and Luo, Kan and He, Jian and Wang, Chong and Du, Shiyu},
+ date = {2023-03-27},
+ journaltitle = {Journal of Chemical Information and Modeling},
+ shortjournal = {J. Chem. Inf. Model.},
+ volume = {63},
+ number = {6},
+ pages = {1756--1765},
+ publisher = {{American Chemical Society}},
+ issn = {1549-9596},
+ doi = {10.1021/acs.jcim.3c00077},
+ url = {https://doi.org/10.1021/acs.jcim.3c00077},
+ urldate = {2023-06-30},
+ abstract = {This paper proposes a new interatomic potential energy neural network, AisNet, which can efficiently predict atomic energies and forces covering different molecular and crystalline materials by encoding universal local environment features, such as elements and atomic positions. Inspired by the framework of SchNet, AisNet consists of an encoding module combining autoencoder with embedding, the triplet loss function and an atomic central symmetry function (ACSF), an interaction module with a periodic boundary condition (PBC), and a prediction module. In molecules, the prediction accuracy of AisNet is comparabel with SchNet on the MD17 dataset, mainly attributed to the effective capture of chemical functional groups through the interaction module. In selected metal and ceramic material datasets, the introduction of ACSF improves the overall accuracy of AisNet by an average of 16.8\% for energy and 28.6\% for force. Furthermore, a close relationship is found between the feature ratio (i.e., ACSF and embedding) and the force prediction errors, exhibiting similar spoon-shaped curves in the datasets of Cu and HfO2. AisNet produces highly accurate predictions in single-commponent alloys with little data, suggesting the encoding process reduces dependence on the number and richness of datasets. Especially for force prediction, AisNet exceeds SchNet by 19.8\% for Al and even 81.2\% higher than DeepMD on a ternary FeCrAl alloy. Capable of processing multivariate features, our model is likely to be applied to a wider range of material systems by incorporating more atomic descriptions.},
+ keywords = {/unread,AML,ML,MLP,universal potential}
+}
+
+@article{huangEmergingTopologicalStates2017,
+ title = {Emerging Topological States in Quasi-Two-Dimensional Materials},
+ author = {Huang, Huaqing and Xu, Yong and Wang, Jianfeng and Duan, Wenhui},
+ date = {2017},
+ journaltitle = {WIREs Computational Molecular Science},
+ volume = {7},
+ number = {4},
+ pages = {e1296},
+ issn = {1759-0884},
+ doi = {10.1002/wcms.1296},
+ url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/wcms.1296},
+ urldate = {2023-06-15},
+ abstract = {Inspired by the discovery of graphene, various two-dimensional (2D) materials have been experimentally realized, which exhibit novel physical properties and support promising applications. Exotic topological states in 2D materials (including quantum spin Hall and quantum anomalous Hall insulators), which are characterized by nontrivial metallic edge states within the insulating bulk gap, have attracted considerable attentions in the past decade due to their great importance for fundamental research and practical applications. They also create a surge of research activities and attract extensive efforts to search for new topological materials in realistic 2D/quasi-2D systems. This review presents a comprehensive survey of recent progress in designing of topological states in quasi-2D materials, including various quantum well heterostructures and 2D atomic lattice structures. In particular, the possibilities of constructing topological nontrivial states from commonly used materials are discussed and the ways of enlarging energy gaps of topological states and realizing different topological states in a single material are presented. WIREs Comput Mol Sci 2017, 7:e1296. doi: 10.1002/wcms.1296 This article is categorized under: Structure and Mechanism {$>$} Computational Materials Science},
+ langid = {english},
+ file = {/Users/wasmer/Nextcloud/Zotero/Huang et al_2017_Emerging topological states in quasi-two-dimensional materials.pdf;/Users/wasmer/Zotero/storage/R26FLUTA/wcms.html}
+}
+
+@article{huangUnveilingComplexStructureproperty2023,
+ title = {Unveiling the Complex Structure-Property Correlation of Defects in {{2D}} Materials Based on High Throughput Datasets},
+ author = {Huang, Pengru and Lukin, Ruslan and Faleev, Maxim and Kazeev, Nikita and Al-Maeeni, Abdalaziz Rashid and Andreeva, Daria V. and Ustyuzhanin, Andrey and Tormasov, Alexander and Castro Neto, A. H. and Novoselov, Kostya S.},
+ date = {2023-02-01},
+ journaltitle = {npj 2D Materials and Applications},
+ shortjournal = {npj 2D Mater Appl},
+ volume = {7},
+ number = {1},
+ pages = {1--10},
+ publisher = {{Nature Publishing Group}},
+ issn = {2397-7132},
+ doi = {10.1038/s41699-023-00369-1},
+ url = {https://www.nature.com/articles/s41699-023-00369-1},
+ urldate = {2023-07-01},
+ abstract = {Modification of physical properties of materials and design of materials with on-demand characteristics is at the heart of modern technology. Rare application relies on pure materials—most devices and technologies require careful design of materials properties through alloying, creating heterostructures of composites, or controllable introduction of defects. At the same time, such designer materials are notoriously difficult to model. Thus, it is very tempting to apply machine learning methods to such systems. Unfortunately, there is only a handful of machine learning-friendly material databases available these days. We develop a platform for easy implementation of machine learning techniques to materials design and populate it with datasets on pristine and defected materials. Here we introduce the 2D Material Defect (2DMD) datasets that include defect properties of represented 2D materials such as MoS2, WSe2, hBN, GaSe, InSe, and black phosphorous, calculated using DFT. Our study provides a data-driven physical understanding of complex behaviors of defect properties in 2D materials, holding promise for a guide to the development of efficient machine learning models. In addition, with the increasing enrollment of datasets, our database could provide a platform for designing materials with predetermined properties.},
+ issue = {1},
+ langid = {english},
+ keywords = {2D material,2DMD dataset,AML,Database,database generation,defect descriptor,defect screening,defects,disordered,high-density defects,High-throughput,HTC,materials screening,ML,MoS2,PBE,physics,point defects,sampling,spin-polarized,TMDC,vacancies,VASP,with-data},
+ file = {/Users/wasmer/Nextcloud/Zotero/Huang et al_2023_Unveiling the complex structure-property correlation of defects in 2D materials.pdf}
+}
+
@article{huberAiiDAScalableComputational2020,
title = {{{AiiDA}} 1.0, a Scalable Computational Infrastructure for Automated Reproducible Workflows and Data Provenance},
author = {Huber, Sebastiaan P. and Zoupanos, Spyros and Uhrin, Martin and Talirz, Leopold and Kahle, Leonid and Häuselmann, Rico and Gresch, Dominik and Müller, Tiziano and Yakutovich, Aliaksandr V. and Andersen, Casper W. and Ramirez, Francisco F. and Adorf, Carl S. and Gargiulo, Fernando and Kumbhar, Snehal and Passaro, Elsa and Johnston, Conrad and Merkys, Andrius and Cepellotti, Andrea and Mounet, Nicolas and Marzari, Nicola and Kozinsky, Boris and Pizzi, Giovanni},
@@ -4284,7 +5227,7 @@
abstract = {The prediction of material properties based on density-functional theory has become routinely common, thanks, in part, to the steady increase in the number and robustness of available simulation packages. This plurality of codes and methods is both a boon and a burden. While providing great opportunities for cross-verification, these packages adopt different methods, algorithms, and paradigms, making it challenging to choose, master, and efficiently use them. We demonstrate how developing common interfaces for workflows that automatically compute material properties greatly simplifies interoperability and cross-verification. We introduce design rules for reusable, code-agnostic, workflow interfaces to compute well-defined material properties, which we implement for eleven quantum engines and use to compute various material properties. Each implementation encodes carefully selected simulation parameters and workflow logic, making the implementer’s expertise of the quantum engine directly available to non-experts. All workflows are made available as open-source and full reproducibility of the workflows is guaranteed through the use of the AiiDA infrastructure.},
issue = {1},
langid = {english},
- keywords = {/unread,AiiDA,AiiDA-FLEUR,DFT,FAIR,FLEUR,provenance,workflows},
+ keywords = {AiiDA,AiiDA-FLEUR,DFT,FAIR,FLEUR,provenance,workflows},
file = {/Users/wasmer/Nextcloud/Zotero/Huber et al_2021_Common workflows for computing material properties using different quantum3.pdf}
}
@@ -4366,6 +5309,26 @@
file = {/Users/wasmer/Zotero/storage/WLWNEY4Q/1.html}
}
+@article{ImprovingEfficiencyInitio2022,
+ title = {Improving the Efficiency of Ab Initio Electronic-Structure Calculations by Deep Learning},
+ date = {2022-07},
+ journaltitle = {Nature Computational Science},
+ shortjournal = {Nat Comput Sci},
+ volume = {2},
+ number = {7},
+ pages = {418--419},
+ publisher = {{Nature Publishing Group}},
+ issn = {2662-8457},
+ doi = {10.1038/s43588-022-00270-9},
+ url = {https://www.nature.com/articles/s43588-022-00270-9},
+ urldate = {2023-06-12},
+ abstract = {A deep neural network method is developed to learn the density functional theory (DFT) Hamiltonian as a function of atomic structure. This approach provides a solution to the accuracy–efficiency dilemma of DFT and opens opportunities to investigate large-scale materials, such as twisted van der Waals materials.},
+ issue = {7},
+ langid = {english},
+ keywords = {\_tablet,2D material,AML,Berry phase,briefing,Computational methods,DeepH,DFT,e3nn,Electronic properties and materials,Electronic structure,equivariant,GGA,graphene,heterostructures,library,materials,ML,ML-DFT,ML-ESM,MPNN,OpenMX,PBE,prediction of bandstructure,prediction of Berry phase,prediction of Hamiltonian matrix,SOC,twisted bilayer graphene,with-code,with-data},
+ file = {/Users/wasmer/Nextcloud/Zotero/2022_Improving the efficiency of ab initio electronic-structure calculations by deep.pdf}
+}
+
@article{ismail-beigiNewAlgebraicFormulation2000,
title = {New {{Algebraic Formulation}} of {{Density Functional Calculation}}},
author = {Ismail-Beigi, Sohrab and Arias, T. A.},
@@ -4406,6 +5369,21 @@
file = {/Users/wasmer/Nextcloud/Zotero/Jablonka et al_2020_Big-Data Science in Porous Materials.pdf}
}
+@online{jablonkaGPT3AllYou2023,
+ title = {Is {{GPT-3}} All You Need for Low-Data Discovery in Chemistry?},
+ author = {Jablonka, Kevin Maik and Schwaller, Philippe and Ortega-Guerrero, Andres and Smit, Berend},
+ date = {2023-02-14},
+ eprinttype = {ChemRxiv},
+ doi = {10.26434/chemrxiv-2023-fw8n4},
+ url = {https://chemrxiv.org/engage/chemrxiv/article-details/63eb5a669da0bc6b33e97a35},
+ urldate = {2023-05-20},
+ abstract = {Machine learning has revolutionized many fields and has recently found applications in chemistry and materials science. The small datasets commonly found in chemistry lead to various sophisticated machine-learning approaches that incorporate chemical knowledge for each application and therefore require a lot of expertise to develop. Here, we show that large language models that have been trained on vast amounts of text extracted from the internet can easily be adapted to solve various tasks in chemistry and materials science by simply prompting them with chemical questions in natural language. We compared this approach with dedicated machine-learning models for many applications spanning properties of molecules and materials to the yield of chemical reactions. Surprisingly, we find this approach performs comparable to or even outperforms the conventional techniques, particularly in the low data limit. In addition, by simply inverting the questions, we can even perform inverse design successfully. The high performance, especially for small data sets, combined with the ease of use, can have a fundamental impact on how we leverage machine learning in the chemical and material sciences. Next to a literature search, querying a foundational model might become a routine way to bootstrap a project by leveraging the collective knowledge encoded in these foundational models.},
+ langid = {english},
+ pubstate = {preprint},
+ keywords = {alternative approaches,AML,chemistry,classification,generative models,GPT,GPT-3,high-entropy alloys,inverse design,IUPAC,library,LLM,ML,pretrained models,property prediction,regression,SELFIES,small data,SMILES,with-code},
+ file = {/Users/wasmer/Nextcloud/Zotero/Jablonka et al_2023_Is GPT-3 all you need for low-data discovery in chemistry.pdf}
+}
+
@article{jacobsMaterialsSimulationToolkit2020,
title = {The {{Materials Simulation Toolkit}} for {{Machine}} Learning ({{MAST-ML}}): {{An}} Automated Open Source Toolkit to Accelerate Data-Driven Materials Research},
shorttitle = {The {{Materials Simulation Toolkit}} for {{Machine}} Learning ({{MAST-ML}})},
@@ -4462,6 +5440,38 @@
file = {/Users/wasmer/Nextcloud/Zotero/Jain et al_2015_FireWorks.pdf;/Users/wasmer/Zotero/storage/FFWIWLTR/cpe.html}
}
+@online{jainGFlowNetsAIDrivenScientific2023,
+ title = {{{GFlowNets}} for {{AI-Driven Scientific Discovery}}},
+ author = {Jain, Moksh and Deleu, Tristan and Hartford, Jason and Liu, Cheng-Hao and Hernandez-Garcia, Alex and Bengio, Yoshua},
+ date = {2023-02-01},
+ eprint = {2302.00615},
+ eprinttype = {arxiv},
+ eprintclass = {cs},
+ doi = {10.48550/arXiv.2302.00615},
+ url = {http://arxiv.org/abs/2302.00615},
+ urldate = {2023-06-23},
+ abstract = {Tackling the most pressing problems for humanity, such as the climate crisis and the threat of global pandemics, requires accelerating the pace of scientific discovery. While science has traditionally relied on trial and error and even serendipity to a large extent, the last few decades have seen a surge of data-driven scientific discoveries. However, in order to truly leverage large-scale data sets and high-throughput experimental setups, machine learning methods will need to be further improved and better integrated in the scientific discovery pipeline. A key challenge for current machine learning methods in this context is the efficient exploration of very large search spaces, which requires techniques for estimating reducible (epistemic) uncertainty and generating sets of diverse and informative experiments to perform. This motivated a new probabilistic machine learning framework called GFlowNets, which can be applied in the modeling, hypotheses generation and experimental design stages of the experimental science loop. GFlowNets learn to sample from a distribution given indirectly by a reward function corresponding to an unnormalized probability, which enables sampling diverse, high-reward candidates. GFlowNets can also be used to form efficient and amortized Bayesian posterior estimators for causal models conditioned on the already acquired experimental data. Having such posterior models can then provide estimators of epistemic uncertainty and information gain that can drive an experimental design policy. Altogether, here we will argue that GFlowNets can become a valuable tool for AI-driven scientific discovery, especially in scenarios of very large candidate spaces where we have access to cheap but inaccurate measurements or to expensive but accurate measurements. This is a common setting in the context of drug and material discovery, which we use as examples throughout the paper.},
+ pubstate = {preprint},
+ keywords = {AML,Bayesian methods,drug discovery,General ML,generative models,materials discovery,MCMC,ML,molecular docking,molecules},
+ file = {/Users/wasmer/Nextcloud/Zotero/Jain et al_2023_GFlowNets for AI-Driven Scientific Discovery.pdf;/Users/wasmer/Zotero/storage/298HNEQA/2302.html}
+}
+
+@online{janssenAutomatedOptimizationConvergence2021,
+ title = {Automated Optimization of Convergence Parameters in Plane Wave Density Functional Theory Calculations via a Tensor Decomposition-Based Uncertainty Quantification},
+ author = {Janssen, Jan and Makarov, Edgar and Hickel, Tilmann and Shapeev, Alexander V. and Neugebauer, Jörg},
+ date = {2021-12-07},
+ eprint = {2112.04081},
+ eprinttype = {arxiv},
+ eprintclass = {cond-mat},
+ doi = {10.48550/arXiv.2112.04081},
+ url = {http://arxiv.org/abs/2112.04081},
+ urldate = {2023-05-26},
+ abstract = {First principles approaches have revolutionized our ability in using computers to predict, explore and design materials. A major advantage commonly associated with these approaches is that they are fully parameter free. However, numerically solving the underlying equations requires to choose a set of convergence parameters. With the advent of high-throughput calculations it becomes exceedingly important to achieve a truly parameter free approach. Utilizing uncertainty quantification (UQ) and tensor decomposition we derive a numerically highly efficient representation of the statistical and systematic error in the multidimensional space of the convergence parameters. Based on this formalism we implement a fully automated approach that requires as input the target accuracy rather than convergence parameters. The performance and robustness of the approach are shown by applying it to a large set of elements crystallizing in a cubic fcc lattice.},
+ pubstate = {preprint},
+ keywords = {/unread,Condensed Matter - Materials Science},
+ file = {/Users/wasmer/Nextcloud/Zotero/Janssen et al_2021_Automated optimization of convergence parameters in plane wave density.pdf;/Users/wasmer/Zotero/storage/JGP5A47W/2112.html}
+}
+
@article{janssenPyironIntegratedDevelopment2019,
title = {Pyiron: {{An}} Integrated Development Environment for Computational Materials Science},
shorttitle = {Pyiron},
@@ -4534,19 +5544,38 @@
title = {Computational Nanoscience: Do It Yourself! : {{Winter School}}, 14-22 {{February}} 2006, {{Forschungszentrum Julich}}, {{Germany}} : Lecture Notes},
shorttitle = {Computational Nanoscience},
editor = {{John von Neumann-Institut für Computing} and {Winter School} and Grotendorst, Johannes and Blügel, Stefan and Marx, Dominik and {John von Neumann-Institut für Computing}},
+ editora = {Mavropoulos, Phivos and Zeller, Rudolf and Lounis, Samir and Dederichs, Peter H.},
+ editoratype = {collaborator},
date = {2006},
publisher = {{John von Neumann Institut for Computing}},
location = {{Julich, Germany}},
url = {http://hdl.handle.net/2128/2943},
- editora = {Mavropoulos, Phivos and Zeller, Rudolf and Lounis, Samir and Dederichs, Peter H.},
- editoratype = {collaborator},
isbn = {978-3-00-017350-9},
langid = {english},
- keywords = {FZJ,KKR,NIC,NIC winter school,PGI-1/IAS-1},
+ keywords = {DFT,electronic structure theory,FZJ,KKR,learning material,magnetism,NIC,NIC winter school,non-collinear,PGI-1/IAS-1,transport properties},
annotation = {OCLC: 77518371},
file = {/Users/wasmer/Nextcloud/Zotero/John von Neumann-Institut für Computing et al_2006_Computational nanoscience.pdf}
}
+@article{jonesDensityFunctionalTheory2015,
+ title = {Density Functional Theory: {{Its}} Origins, Rise to Prominence, and Future},
+ shorttitle = {Density Functional Theory},
+ author = {Jones, R. O.},
+ date = {2015-08-25},
+ journaltitle = {Reviews of Modern Physics},
+ shortjournal = {Rev. Mod. Phys.},
+ volume = {87},
+ number = {3},
+ pages = {897--923},
+ publisher = {{American Physical Society}},
+ doi = {10.1103/RevModPhys.87.897},
+ url = {https://link.aps.org/doi/10.1103/RevModPhys.87.897},
+ urldate = {2023-06-30},
+ abstract = {In little more than 20 years, the number of applications of the density functional (DF) formalism in chemistry and materials science has grown in an astonishing fashion. The number of publications alone shows that DF calculations make up a huge success story, and many younger colleagues are surprised to learn that the widespread application of density functional methods, particularly in chemistry, began only after 1990. This is indeed unexpected, because the origins are usually traced to the papers of Hohenberg, Kohn, and Sham more than a quarter of a century earlier. The DF formalism, its applications, and prospects were reviewed for this journal in 1989. About the same time, the combination of DF calculations with molecular dynamics promised to provide an efficient way to study structures and reactions in molecules and extended systems. This paper reviews the development of density-related methods back to the early years of quantum mechanics and follows the breakthrough in their application after 1990. The two examples from biochemistry and materials science are among the many current applications that were simply far beyond expectations in 1990. The reasons why—50 years after its modern formulation and after two decades of rapid expansion—some of the most cited practitioners in the field are concerned about its future are discussed.},
+ keywords = {DFT,FZJ,PGI-1/IAS-1,review,review-of-DFT},
+ file = {/Users/wasmer/Nextcloud/Zotero/Jones_2015_Density functional theory.pdf;/Users/wasmer/Zotero/storage/Z29CZB7G/RevModPhys.87.html}
+}
+
@online{jorgensenDeepDFTNeuralMessage2020,
title = {{{DeepDFT}}: {{Neural Message Passing Network}} for {{Accurate Charge Density Prediction}}},
shorttitle = {{{DeepDFT}}},
@@ -4560,7 +5589,7 @@
urldate = {2022-07-10},
abstract = {We introduce DeepDFT, a deep learning model for predicting the electronic charge density around atoms, the fundamental variable in electronic structure simulations from which all ground state properties can be calculated. The model is formulated as neural message passing on a graph, consisting of interacting atom vertices and special query point vertices for which the charge density is predicted. The accuracy and scalability of the model are demonstrated for molecules, solids and liquids. The trained model achieves lower average prediction errors than the observed variations in charge density obtained from density functional theory simulations using different exchange correlation functionals.},
pubstate = {preprint},
- keywords = {Computer Science - Machine Learning,Physics - Computational Physics},
+ keywords = {\_tablet,AML,DeepDFT,disordered,electrochemistry,equivariant,GNN,invariance,library,linear scaling,liquids,materials,ML,ML-DFT,ML-ESM,molecules,MPNN,NN,organic chemistry,original publication,oxides,PAiNN,prediction of electron density,PyTorch,QM9,SchNet,transition metals,VASP,with-code,with-data},
file = {/Users/wasmer/Nextcloud/Zotero/Jørgensen_Bhowmik_2020_DeepDFT.pdf;/Users/wasmer/Zotero/storage/QXJKV745/2011.html}
}
@@ -4581,7 +5610,7 @@
abstract = {Electron density \$\$\textbackslash rho (\textbackslash overrightarrow\{\{\{\{\textbackslash bf\{r\}\}\}\}\})\$\$is the fundamental variable in the calculation of ground state energy with density functional theory (DFT). Beyond total energy, features and changes in \$\$\textbackslash rho (\textbackslash overrightarrow\{\{\{\{\textbackslash bf\{r\}\}\}\}\})\$\$distributions are often used to capture critical physicochemical phenomena in functional materials. We present a machine learning framework for the prediction of \$\$\textbackslash rho (\textbackslash overrightarrow\{\{\{\{\textbackslash bf\{r\}\}\}\}\})\$\$. The model is based on equivariant graph neural networks and the electron density is predicted at special query point vertices that are part of the message-passing graph, but only receive messages. The model is tested across multiple datasets of molecules (QM9), liquid ethylene carbonate electrolyte (EC) and LixNiyMnzCo(1-y-z)O2 lithium ion battery cathodes (NMC). For QM9 molecules, the accuracy of the proposed model exceeds typical variability in \$\$\textbackslash rho (\textbackslash overrightarrow\{\{\{\{\textbackslash bf\{r\}\}\}\}\})\$\$obtained from DFT done with different exchange-correlation functionals. The accuracy on all three datasets is beyond state of the art and the computation time is orders of magnitude faster than DFT.},
issue = {1},
langid = {english},
- keywords = {AML,DeepDFT,equivariant,GNN,liquids,materials,ML,ML-DFT,ML-ESM,molecules,MPNN,NN,prediction of electron density,QM9,VASP,with-data},
+ keywords = {\_tablet,AML,DeepDFT,disordered,electrochemistry,equivariant,GNN,invariance,library,linear scaling,liquids,materials,ML,ML-DFT,ML-ESM,molecules,MPNN,NN,organic chemistry,original publication,oxides,PAiNN,prediction of electron density,PyTorch,QM9,SchNet,transition metals,VASP,with-code,with-data},
file = {/Users/wasmer/Nextcloud/Zotero/Jørgensen_Bhowmik_2022_Equivariant graph neural networks for fast electron density estimation of.pdf}
}
@@ -4597,7 +5626,7 @@
urldate = {2022-07-10},
abstract = {Electron density \$\textbackslash rho(\textbackslash vec\{r\})\$ is the fundamental variable in the calculation of ground state energy with density functional theory (DFT). Beyond total energy, features in \$\textbackslash rho(\textbackslash vec\{r\})\$ distribution and modifications in \$\textbackslash rho(\textbackslash vec\{r\})\$ are often used to capture critical physicochemical phenomena in functional materials and molecules at the electronic scale. Methods providing access to \$\textbackslash rho(\textbackslash vec\{r\})\$ of complex disordered systems with little computational cost can be a game changer in the expedited exploration of materials phase space towards the inverse design of new materials with better functionalities. We present a machine learning framework for the prediction of \$\textbackslash rho(\textbackslash vec\{r\})\$. The model is based on equivariant graph neural networks and the electron density is predicted at special query point vertices that are part of the message passing graph, but only receive messages. The model is tested across multiple data sets of molecules (QM9), liquid ethylene carbonate electrolyte (EC) and LixNiyMnzCo(1-y-z)O2 lithium ion battery cathodes (NMC). For QM9 molecules, the accuracy of the proposed model exceeds typical variability in \$\textbackslash rho(\textbackslash vec\{r\})\$ obtained from DFT done with different exchange-correlation functional and show beyond the state of the art accuracy. The accuracy is even better for the mixed oxide (NMC) and electrolyte (EC) datasets. The linear scaling model's capacity to probe thousands of points simultaneously permits calculation of \$\textbackslash rho(\textbackslash vec\{r\})\$ for large complex systems many orders of magnitude faster than DFT allowing screening of disordered functional materials.},
pubstate = {preprint},
- keywords = {Physics - Computational Physics,Statistics - Machine Learning},
+ keywords = {\_tablet,AML,DeepDFT,disordered,electrochemistry,equivariant,GNN,invariance,library,linear scaling,liquids,materials,ML,ML-DFT,ML-ESM,molecules,MPNN,NN,organic chemistry,original publication,oxides,PAiNN,prediction of electron density,PyTorch,QM9,SchNet,transition metals,VASP,with-code,with-data},
file = {/Users/wasmer/Nextcloud/Zotero/Jørgensen_Bhowmik_2021_Graph neural networks for fast electron density estimation of molecules,.pdf;/Users/wasmer/Zotero/storage/MBXG22TT/2112.html}
}
@@ -4628,6 +5657,24 @@
file = {/Users/wasmer/Nextcloud/Zotero/Kaba_Ravanbakhsh_2023_Equivariant Networks for Crystal Structures.pdf;/Users/wasmer/Zotero/storage/2YADPZ3J/2211.html}
}
+@article{kabaPredictionLargeMagnetic2023,
+ title = {Prediction of Large Magnetic Moment Materials with Graph Neural Networks and Random Forests},
+ author = {Kaba, Sékou-Oumar and Groleau-Paré, Benjamin and Gauthier, Marc-Antoine and Tremblay, A.-M. S. and Verret, Simon and Gauvin-Ndiaye, Chloé},
+ date = {2023-04-14},
+ journaltitle = {Physical Review Materials},
+ shortjournal = {Phys. Rev. Mater.},
+ volume = {7},
+ number = {4},
+ pages = {044407},
+ publisher = {{American Physical Society}},
+ doi = {10.1103/PhysRevMaterials.7.044407},
+ url = {https://link.aps.org/doi/10.1103/PhysRevMaterials.7.044407},
+ urldate = {2023-06-01},
+ abstract = {Magnetic materials are crucial components of many technologies that could drive the ecological transition, including electric motors, wind turbine generators, and magnetic refrigeration systems. Discovering materials with large magnetic moments is therefore an increasing priority. Here, using state-of-the-art machine learning methods, we scan the Inorganic Crystal Structure Database (ICSD) of hundreds of thousands of existing materials to find those that are ferromagnetic and have large magnetic moments. Crystal graph convolutional neural networks (CGCNNs), materials graph network (MEGNet), and random forests are trained on the Materials Project database that contains the results of high-throughput density functional theory (DFT) predictions. For random forests, we use a stochastic method to select nearly 100 relevant descriptors based on chemical composition and crystal structure. This gives results that are comparable to those of neural networks. Our findings suggests that magnetic properties are intrinsically more difficult to predict than other DFT-calculated properties. The comparison between the different machine learning approaches gives an estimate of the errors for our predictions on the ICSD database. Validating our final predictions by comparisons with available experimental data, we found 15 materials that are likely to have large magnetic moments and have not yet been studied experimentally.},
+ keywords = {\_tablet,AML,benchmarking,CGCNN,compositional descriptors,descriptors,DFT,Ferromagnetism,GGA,GGA+U,GNN,HTC,ICSD,magnetic moment,magnetism,magnetocaloric,materials,materials project,materials screening,MEGNet,ML,model comparison,prediction from structure,prediction of magnetic moment,random forest,rare earths,transition metals},
+ file = {/Users/wasmer/Nextcloud/Zotero/Kaba et al_2023_Prediction of large magnetic moment materials with graph neural networks and.pdf;/Users/wasmer/Zotero/storage/ES8ANMU8/PhysRevMaterials.7.html}
+}
+
@article{kajitaDiscoverySuperionicConductors2020,
title = {Discovery of Superionic Conductors by Ensemble-Scope Descriptor},
author = {Kajita, Seiji and Ohba, Nobuko and Suzumura, Akitoshi and Tajima, Shin and Asahi, Ryoji},
@@ -4720,6 +5767,24 @@
file = {/Users/wasmer/Nextcloud/Zotero/Kamal et al_2020_A charge density prediction model for hydrocarbons using deep neural networks.pdf}
}
+@article{kandyComparingTransferabilityNeural2023,
+ title = {Comparing Transferability in Neural Network Approaches and Linear Models for Machine-Learning Interaction Potentials},
+ author = {Kandy, Akshay Krishna Ammothum and Rossi, Kevin and Raulin-Foissac, Alexis and Laurens, Gaétan and Lam, Julien},
+ date = {2023-05-10},
+ journaltitle = {Physical Review B},
+ shortjournal = {Phys. Rev. B},
+ volume = {107},
+ number = {17},
+ pages = {174106},
+ publisher = {{American Physical Society}},
+ doi = {10.1103/PhysRevB.107.174106},
+ url = {https://link.aps.org/doi/10.1103/PhysRevB.107.174106},
+ urldate = {2023-06-30},
+ abstract = {Atomic simulations using machine learning interatomic potential (MLIP) have gained a lot of popularity owing to their accuracy in comparison to conventional empirical potentials. However, the transferability of MLIP to systems outside the training set poses a significant challenge. Here, we compare the transferability of three MLIP approaches: (i) neural network potentials (NNP), (ii) physical LassoLars interactions potential (PLIP) and (iii) linear potentials with Belher-Parrinello descriptors, trained over a small but diverse configuration of zinc oxide polymorphs. We compared the obtained models with density functional theory reference results for physical properties including bulk lattice parameters, surface energies, and vibrational density of states and showed the superiority of both NNP and PLIP models. However, the NNP model performed poorly when compared to the other two linear models for the structural optimization of nanoparticles and molecular dynamics simulation of liquid phases, which are systems outside the training set. While providing less accurate prediction for solid Zinc Oxides phases, both linear models appear more transferable than NNP when testing for nanoscale systems and liquid phases. Our results are finally rationalized by a combination of different statistical analysis including spread in force evaluation, information imbalance, convex hull calculation, and density in descriptor space.},
+ keywords = {AML,benchmarking,BPNN,descriptor comparison,liquids,ML,MLP,MLP comparison,nanomaterials,NNP,PLIP,transfer learning},
+ file = {/Users/wasmer/Nextcloud/Zotero/Kandy et al_2023_Comparing transferability in neural network approaches and linear models for.pdf;/Users/wasmer/Zotero/storage/TZPHFWS9/PhysRevB.107.html}
+}
+
@inproceedings{kanterDeepFeatureSynthesis2015,
title = {Deep Feature Synthesis: {{Towards}} Automating Data Science Endeavors},
shorttitle = {Deep Feature Synthesis},
@@ -4769,10 +5834,52 @@
urldate = {2022-06-30},
abstract = {Machine learning (ML)-based models have greatly enhanced the traditional materials discovery and design pipeline. Specifically, in recent years, surrogate ML models for material property prediction have demonstrated success in predicting discrete scalar-valued target properties to within reasonable accuracy of their DFT-computed values. However, accurate prediction of spectral targets, such as the electron density of states (DOS), poses a much more challenging problem due to the complexity of the target, and the limited amount of available training data. In this study, we present an extension of the recently developed atomistic line graph neural network to accurately predict DOS of a large set of material unit cell structures, trained to the publicly available JARVIS-DFT dataset. Furthermore, we evaluate two methods of representation of the target quantity: a direct discretized spectrum, and a compressed low-dimensional representation obtained using an autoencoder. Through this work, we demonstrate the utility of graph-based featurization and modeling methods in the prediction of complex targets that depend on both chemistry and directional characteristics of material structures.},
langid = {english},
- keywords = {_tablet,ALIGNN,autoencoder,DFT,dimensionality reduction,dimensionality reduction of target,GNN,JARVIS,JARVIS-DFT,ML,ML-DFT,ML-ESM,MPNN,prediction from structure,prediction of LDOS},
+ keywords = {\_tablet,ALIGNN,autoencoder,DFT,dimensionality reduction,dimensionality reduction of target,GNN,JARVIS,JARVIS-DFT,ML,ML-DFT,ML-ESM,MPNN,prediction from structure,prediction of LDOS},
file = {/Users/wasmer/Nextcloud/Zotero/Kaundinya et al_2022_Prediction of the Electron Density of States for Crystalline Compounds with.pdf}
}
+@article{kawamuraLaughlinChargePumping2023,
+ title = {Laughlin Charge Pumping in a Quantum Anomalous {{Hall}} Insulator},
+ author = {Kawamura, Minoru and Mogi, Masataka and Yoshimi, Ryutaro and Morimoto, Takahiro and Takahashi, Kei S. and Tsukazaki, Atsushi and Nagaosa, Naoto and Kawasaki, Masashi and Tokura, Yoshinori},
+ date = {2023-03},
+ journaltitle = {Nature Physics},
+ shortjournal = {Nat. Phys.},
+ volume = {19},
+ number = {3},
+ pages = {333--337},
+ publisher = {{Nature Publishing Group}},
+ issn = {1745-2481},
+ doi = {10.1038/s41567-022-01888-2},
+ url = {https://www.nature.com/articles/s41567-022-01888-2},
+ urldate = {2023-05-08},
+ abstract = {Adiabatic charge pumping is one of the most salient features of topological phases of matter1–3. Laughlin’s charge pumping in a quantum Hall system is the prototypical example4. In analogy, three-dimensional topological insulators have been predicted to support charge pumping through their magnetically gapped surface states5–10. But despite its importance as a direct probe of surface Hall conductivity, charge pumping has not been demonstrated in topological-insulator-based systems. Here we report the observation of charge pumping in a thin-film magnetic heterostructure of topological insulators in a geometry that prohibits edge transport. We find that charge pumping occurs between the inner and outer electrodes in response to alternating magnetic fields when the sample is in the quantum anomalous Hall insulator phase. The amount of pumped charge is accounted for by the surface Hall conductivity of half the quantum conductance for each surface, from a comparison with the axion insulator phase that shows no charge pumping. Because charge pumping is closely related to the theoretically predicted topological magnetoelectric effect5–10, our observation may provide clues to its direct observation.},
+ issue = {3},
+ langid = {english},
+ keywords = {/unread,experimental,Hall QAHE,interfaces and thin films,surface physics,topological insulator},
+ file = {/Users/wasmer/Nextcloud/Zotero/Kawamura et al_2023_Laughlin charge pumping in a quantum anomalous Hall insulator.pdf}
+}
+
+@article{kazeevSparseRepresentationMachine2023,
+ title = {Sparse Representation for Machine Learning the Properties of Defects in {{2D}} Materials},
+ author = {Kazeev, Nikita and Al-Maeeni, Abdalaziz Rashid and Romanov, Ignat and Faleev, Maxim and Lukin, Ruslan and Tormasov, Alexander and Castro Neto, A. H. and Novoselov, Kostya S. and Huang, Pengru and Ustyuzhanin, Andrey},
+ date = {2023-06-26},
+ journaltitle = {npj Computational Materials},
+ shortjournal = {npj Comput Mater},
+ volume = {9},
+ number = {1},
+ pages = {1--10},
+ publisher = {{Nature Publishing Group}},
+ issn = {2057-3960},
+ doi = {10.1038/s41524-023-01062-z},
+ url = {https://www.nature.com/articles/s41524-023-01062-z},
+ urldate = {2023-07-01},
+ abstract = {Two-dimensional materials offer a promising platform for the next generation of (opto-) electronic devices and other high technology applications. One of the most exciting characteristics of 2D crystals is the ability to tune their properties via controllable introduction of defects. However, the search space for such structures is enormous, and ab-initio computations prohibitively expensive. We propose a machine learning approach for rapid estimation of the properties of 2D material given the lattice structure and defect configuration. The method suggests a way to represent configuration of 2D materials with defects that allows a neural network to train quickly and accurately. We compare our methodology with the state-of-the-art approaches and demonstrate at least 3.7 times energy prediction error drop. Also, our approach is an order of magnitude more resource-efficient than its contenders both for the training and inference part.},
+ issue = {1},
+ langid = {english},
+ keywords = {2D material,AML,defects,disordered,exchange interaction,GNN,library,ML,point defects,prediction from defect structure,prediction of formation energy,prediction of HOMO/LUMO,with-code,with-data,with-demo},
+ file = {/Users/wasmer/Nextcloud/Zotero/Kazeev et al_2023_Sparse representation for machine learning the properties of defects in 2D.pdf}
+}
+
@article{keimerPhysicsQuantumMaterials2017,
title = {The Physics of Quantum Materials},
author = {Keimer, B. and Moore, J. E.},
@@ -4934,6 +6041,25 @@
file = {/Users/wasmer/Nextcloud/Zotero/Klus et al_2021_Symmetric and antisymmetric kernels for machine learning problems in quantum.pdf;/Users/wasmer/Zotero/storage/WM8YDGB2/2103.html}
}
+@inproceedings{knodelHELIPORTPortablePlatform2021,
+ title = {{{HELIPORT}}: {{A Portable Platform}} for \{\vphantom\}{{FAIR Workflow}} | {{Metadata}} | {{Scientific Project Lifecycle}}\vphantom\{\} {{Management}} and {{Everything}}},
+ shorttitle = {{{HELIPORT}}},
+ booktitle = {Proceedings of the 4th {{International Workshop}} on {{Practical Reproducible Evaluation}} of {{Computer Systems}}},
+ author = {Knodel, Oliver and Voigt, Martin and Ufer, Robert and Pape, David and Lokamani, Mani and Müller, Stefan E. and Gruber, Thomas and Juckeland, Guido},
+ date = {2021-06-11},
+ series = {P-{{RECS}} '21},
+ pages = {9--14},
+ publisher = {{Association for Computing Machinery}},
+ location = {{New York, NY, USA}},
+ doi = {10.1145/3456287.3465477},
+ url = {https://dl.acm.org/doi/10.1145/3456287.3465477},
+ urldate = {2023-05-15},
+ abstract = {Modern scientific collaborations and projects (MSCPs) employ various processing stages, starting with the proposal submission, continuing with data acquisition and concluding with final publications. The realization of such MSCPs poses a huge challenge due to (1) the complexity and diversity of the tools, (2) the heterogeneity of various involved computing and experimental platforms, (3) flexibility of analysis targets towards data acquisition and (4) data throughput. Another challenge for MSCPs is to provide additional metadata according to the FAIR principles for all processing stages for internal and external use. Consequently, the demand for a system, that assists the scientist in all project stages and archives all processes on the basis of metadata standards like DataCite to make really everything transparent, understandable and citable, has risen considerably. The aim of this project is the development of the HELmholtz ScIentific Project WORkflow PlaTform (HELIPORT), which ensures data provenance by accommodating the complete life cycle of a scientific project and linking all employed programs and systems. The modular structure of HELIPORT enables the deployment of the core applications to different Helmholtz centers (HZs) and can be adapted to center-specific needs simply by adding or replacing individual components. HELIPORT is based on modern web technologies and can be used on different platforms.},
+ isbn = {978-1-4503-8395-0},
+ keywords = {CWL,Data management,data provenance,digital objects,Django,experimental,FAIR,Helmholtz,HZDR,metadata,Open source,RabbitMQ,RDM,related identifier,reproducibility,RSE,software,UNICORE,web app,with-code,workflows},
+ file = {/Users/wasmer/Nextcloud/Zotero/Knodel et al_2021_HELIPORT.pdf}
+}
+
@unpublished{knosgaardRepresentingIndividualElectronic2021,
title = {Representing Individual Electronic States in Crystals for Machine Learning Quasiparticle Band Structures},
author = {Knøsgaard, Nikolaj Rørbæk and Thygesen, Kristian Sommer},
@@ -5081,6 +6207,26 @@
file = {/Users/wasmer/Nextcloud/Zotero/Kohn_1965_Self-Consistent Equations Including Exchange and Correlation Effects.pdf;/Users/wasmer/Zotero/storage/4CF9DCKS/PhysRev.140.html}
}
+@article{kongOpportunitiesChemistryMaterials2011,
+ title = {Opportunities in Chemistry and Materials Science for Topological Insulators and Their Nanostructures},
+ author = {Kong, Desheng and Cui, Yi},
+ date = {2011-11},
+ journaltitle = {Nature Chemistry},
+ shortjournal = {Nature Chem},
+ volume = {3},
+ number = {11},
+ pages = {845--849},
+ publisher = {{Nature Publishing Group}},
+ issn = {1755-4349},
+ doi = {10.1038/nchem.1171},
+ url = {https://www.nature.com/articles/nchem.1171},
+ urldate = {2023-06-14},
+ abstract = {Topological insulators — insulators or semiconductors with metallic states present at their boundaries — are the 'rising stars' of condensed-matter physics. This Perspective introduces these materials and their properties, and looks at the challenges and opportunities the community faces.},
+ issue = {11},
+ langid = {english},
+ keywords = {/unread}
+}
+
@article{korshunovaOpenChemDeepLearning2021,
title = {{{OpenChem}}: {{A Deep Learning Toolkit}} for {{Computational Chemistry}} and {{Drug Design}}},
shorttitle = {{{OpenChem}}},
@@ -5129,7 +6275,7 @@
abstract = {Machine learning applications often need large amounts of training data to perform well. Whereas unlabeled data can be easily gathered, the labeling process is difficult, time-consuming, or expensive in most applications. Active learning can help solve this problem by querying labels for those data points that will improve the performance the most. Thereby, the goal is that the learning algorithm performs sufficiently well with fewer labels. We provide a library called scikit-activeml that covers the most relevant query strategies and implements tools to work with partially labeled data. It is programmed in Python and builds on top of scikit-learn.},
langid = {english},
pubstate = {preprint},
- keywords = {/unread,active learning,General ML,library,Python,scikit-learn},
+ keywords = {\_tablet,/unread,active learning,General ML,library,Python,scikit-learn},
file = {/Users/wasmer/Nextcloud/Zotero/Kottke et al_2021_scikit-activeml.pdf}
}
@@ -5147,10 +6293,27 @@
urldate = {2023-04-06},
abstract = {When the magnetic order is introduced into topological insulators (TIs), the time-reversal symmetry (TRS) is broken, and the non-trivial topological surface is driven into a new massive Dirac fermions state. The study of such TRS-breaking systems is one of the most emerging frontiers in condensed-matter physics. In this review, we outline the methods to break the TRS of the topological surface states. With robust out-of-plane magnetic order formed, we describe the intrinsic magnetisms in the magnetically doped 3D TI materials and the approach to manipulate each contribution. Most importantly, we summarize the theoretical developments and experimental observations of the scale-invariant quantum anomalous Hall effect (QAHE) in both the 2D and 3D Cr-doped (BiSb)2Te3 systems; at the same time, we also discuss the correlations between QAHE and other quantum transport phenomena. Finally, we highlight the use of TI/Cr-doped TI heterostructures to both manipulate the surface-related ferromagnetism and realize electrical manipulation of magnetization through the giant spin–orbit torques.},
langid = {english},
- keywords = {breaking of TRS,defects,Hall effect,Hall QAHE,heterostructures,magnetic doping,magnetic heterostructures,magnetism,physics,review,SOC,spin-dependent,Spin-orbit effects,topological insulator,TRS},
+ keywords = {\_tablet,breaking of TRS,defects,Hall effect,Hall QAHE,heterostructures,magnetic doping,magnetic heterostructures,magnetism,physics,review,SOC,spin-dependent,Spin-orbit effects,topological insulator,TRS},
file = {/Users/wasmer/Nextcloud/Zotero/Kou et al_2015_Magnetic topological insulators and quantum anomalous hall effect.pdf;/Users/wasmer/Zotero/storage/IMCZD9AZ/S0038109814004438.html}
}
+@online{kovacsEvaluationMACEForce2023,
+ title = {Evaluation of the {{MACE Force Field Architecture}}: From {{Medicinal Chemistry}} to {{Materials Science}}},
+ shorttitle = {Evaluation of the {{MACE Force Field Architecture}}},
+ author = {Kovacs, David Peter and Batatia, Ilyes and Arany, Eszter Sara and Csanyi, Gabor},
+ date = {2023-05-23},
+ eprint = {2305.14247},
+ eprinttype = {arxiv},
+ eprintclass = {physics, stat},
+ doi = {10.48550/arXiv.2305.14247},
+ url = {http://arxiv.org/abs/2305.14247},
+ urldate = {2023-05-26},
+ abstract = {The MACE architecture represents the state of the art in the field of machine learning force fields for a variety of in-domain, extrapolation and low-data regime tasks. In this paper, we further evaluate MACE by fitting models for published benchmark datasets. We show that MACE generally outperforms alternatives for a wide range of systems from amorphous carbon and general small molecule organic chemistry to large molecules and liquid water. We demonstrate the capabilities of the model on tasks ranging from constrained geometry optimisation to molecular dynamics simulations and find excellent performance across all tested domains. We show that MACE is very data efficient, and can reproduce experimental molecular vibrational spectra when trained on as few as 50 randomly selected reference configurations. We further demonstrate that the strictly local atom-centered model is sufficient for such tasks even in the case of large molecules and weakly interacting molecular assemblies.},
+ pubstate = {preprint},
+ keywords = {ACE,AML,benchmarking,carbon,disordered,GNN,M3GNet,MACE,materials,ML,ML-FF,MLP,model comparison,model evaluation,molecules,MPNN,NewtonNet,QM9},
+ file = {/Users/wasmer/Nextcloud/Zotero/Kovacs et al_2023_Evaluation of the MACE Force Field Architecture.pdf;/Users/wasmer/Zotero/storage/NJT7SCKS/2305.html}
+}
+
@online{krennPredictingFutureAI2022,
title = {Predicting the {{Future}} of {{AI}} with {{AI}}: {{High-quality}} Link Prediction in an Exponentially Growing Knowledge Network},
shorttitle = {Predicting the {{Future}} of {{AI}} with {{AI}}},
@@ -5236,10 +6399,27 @@
url = {https://doi.org/10.1021/acs.chemrev.0c00732},
urldate = {2021-05-13},
abstract = {Topology, a mathematical concept, has recently become a popular and truly transdisciplinary topic encompassing condensed matter physics, solid state chemistry, and materials science. Since there is a direct connection between real space, namely atoms, valence electrons, bonds, and orbitals, and reciprocal space, namely bands and Fermi surfaces, via symmetry and topology, classifying topological materials within a single-particle picture is possible. Currently, most materials are classified as trivial insulators, semimetals, and metals or as topological insulators, Dirac and Weyl nodal-line semimetals, and topological metals. The key ingredients for topology are certain symmetries, the inert pair effect of the outer electrons leading to inversion of the conduction and valence bands, and spin–orbit coupling. This review presents the topological concepts related to solids from the viewpoint of a solid-state chemist, summarizes techniques for growing single crystals, and describes basic physical property measurement techniques to characterize topological materials beyond their structure and provide examples of such materials. Finally, a brief outlook on the impact of topology in other areas of chemistry is provided at the end of the article.},
- keywords = {_tablet,chemistry,topological insulator},
+ keywords = {\_tablet,chemistry,topological insulator},
file = {/Users/wasmer/Nextcloud/Zotero/Kumar et al_2021_Topological Quantum Materials from the Viewpoint of Chemistry.pdf}
}
+@online{laaksoUpdatesDScribeLibrary2023,
+ title = {Updates to the {{DScribe Library}}: {{New Descriptors}} and {{Derivatives}}},
+ shorttitle = {Updates to the {{DScribe Library}}},
+ author = {Laakso, Jarno and Himanen, Lauri and Homm, Henrietta and Morooka, Eiaki V. and Jäger, Marc O. J. and Todorović, Milica and Rinke, Patrick},
+ date = {2023-03-24},
+ eprint = {2303.14046},
+ eprinttype = {arxiv},
+ eprintclass = {cond-mat},
+ doi = {10.48550/arXiv.2303.14046},
+ url = {http://arxiv.org/abs/2303.14046},
+ urldate = {2023-06-12},
+ abstract = {We present an update of the DScribe package, a Python library for atomistic descriptors. The update extends DScribe's descriptor selection with the Valle-Oganov materials fingerprint and provides descriptor derivatives to enable more advanced machine learning tasks, such as force prediction and structure optimization. For all descriptors, numeric derivatives are now available in DSribe. For the many-body tensor representation (MBTR) and the Smooth Overlap of Atomic Positions (SOAP), we have also implemented analytic derivatives. We demonstrate the effectiveness of the descriptor derivatives for machine learning models of Cu clusters and perovskite alloys.},
+ pubstate = {preprint},
+ keywords = {\_tablet,/unread,ACSF,analytical derivatives,derivatives,descriptors,DScribe,library,materials,Matrix descriptors,MBTR,ML,Open source,Python,rec-by-ruess,SOAP,Valle-Oganov descriptor,with-code},
+ file = {/Users/wasmer/Nextcloud/Zotero/Laakso et al_2023_Updates to the DScribe Library.pdf;/Users/wasmer/Zotero/storage/JNKQJJYE/2303.html}
+}
+
@online{labrie-boulayMachineLearningbasedSpin2023,
title = {Machine Learning-Based Spin Structure Detection},
author = {Labrie-Boulay, Isaac and Winkler, Thomas Brian and Franzen, Daniel and Romanova, Alena and Fangohr, Hans and Kläui, Mathias},
@@ -5299,7 +6479,7 @@
url = {http://arxiv.org/abs/2003.12081},
urldate = {2021-05-13},
abstract = {Computational study of molecules and materials from first principles is a cornerstone of physics, chemistry, and materials science, but limited by the cost of accurate and precise simulations. In settings involving many simulations, machine learning can reduce these costs, often by orders of magnitude, by interpolating between reference simulations. This requires representations that describe any molecule or material and support interpolation. We comprehensively review and discuss current representations and relations between them, using a unified mathematical framework based on many-body functions, group averaging, and tensor products. For selected state-of-the-art representations, we compare energy predictions for organic molecules, binary alloys, and Al-Ga-In sesquioxides in numerical experiments controlled for data distribution, regression method, and hyper-parameter optimization.},
- keywords = {_tablet,ACE,benchmarking,BoB,BS,CM,descriptors,GPR,KRR,library,materials,MBTR,ML,models,MTP,review,SOAP,with-code},
+ keywords = {\_tablet,ACE,benchmarking,BoB,BS,CM,descriptors,GPR,KRR,library,materials,MBTR,ML,models,MTP,review,SOAP,with-code},
file = {/Users/wasmer/Nextcloud/Zotero/Langer et al_2021_Representations of molecules and materials for interpolation of.pdf;/Users/wasmer/Zotero/storage/5BG77UWY/2003.html}
}
@@ -5320,7 +6500,7 @@
abstract = {Computational study of molecules and materials from first principles is a cornerstone of physics, chemistry, and materials science, but limited by the cost of accurate and precise simulations. In settings involving many simulations, machine learning can reduce these costs, often by orders of magnitude, by interpolating between reference simulations. This requires representations that describe any molecule or material and support interpolation. We comprehensively review and discuss current representations and relations between them. For selected state-of-the-art representations, we compare energy predictions for organic molecules, binary alloys, and Al–Ga–In sesquioxides in numerical experiments controlled for data distribution, regression method, and hyper-parameter optimization.},
issue = {1},
langid = {english},
- keywords = {_tablet,ACE,autoML,benchmarking,BoB,BS,CM,descriptor comparison,descriptors,GPR,hyperparameters optimization,KRR,library,materials,MBTR,ML,models,MTP,review,SOAP,with-code},
+ keywords = {\_tablet,ACE,autoML,benchmarking,BoB,BS,CM,descriptor comparison,descriptors,GPR,hyperparameters optimization,KRR,library,materials,MBTR,ML,model reporting,models,MTP,review,SOAP,with-code},
file = {/Users/wasmer/Nextcloud/Zotero/Langer et al_2022_Representations of molecules and materials for interpolation of.pdf;/Users/wasmer/Nextcloud/Zotero/Langer et al_2022_Representations of molecules and materials for interpolation of2.pdf;/Users/wasmer/Zotero/storage/9RVUDSSX/s41524-022-00721-x.html}
}
@@ -5454,7 +6634,7 @@
url = {http://arxiv.org/abs/2106.05364},
urldate = {2021-06-29},
abstract = {We introduce a local machine-learning method for predicting the electron densities of periodic systems. The framework is based on a numerical, atom-centred auxiliary basis, which enables an accurate expansion of the all-electron density in a form suitable for learning isolated and periodic systems alike. We show that using this formulation the electron densities of metals, semiconductors and molecular crystals can all be accurately predicted using a symmetry-adapted Gaussian process regression model, properly adjusted for the non-orthogonal nature of the basis. These predicted densities enable the efficient calculation of electronic properties which present errors on the order of tens of meV/atom when compared to ab initio density-functional calculations. We demonstrate the key power of this approach by using a model trained on ice unit cells containing only 4 water molecules to predict the electron densities of cells containing up to 512 molecules, and see no increase in the magnitude of the errors of derived electronic properties when increasing the system size. Indeed, we find that these extrapolated derived energies are more accurate than those predicted using a direct machine-learning model.},
- keywords = {DFT,GPR,lambda-SOAP,library,ML,ML-DFT,ML-ESM,models,molecules,molecules & solids,prediction of electron density,prediction of ground-state properties,Resolution of the identity,SA-GPR,SALTED,solids,with-code},
+ keywords = {DFT,GPR,lambda-SOAP,library,ML,ML-DFT,ML-ESM,models,molecules,molecules \& solids,prediction of electron density,prediction of ground-state properties,Resolution of the identity,SA-GPR,SALTED,solids,with-code},
file = {/home/johannes/Nextcloud/Zotero/false;/Users/wasmer/Nextcloud/Zotero/Lewis et al_2021_Learning electron densities in the condensed-phase.pdf;/Users/wasmer/Zotero/storage/IC2NJGYT/2106.html}
}
@@ -5473,10 +6653,64 @@
url = {https://doi.org/10.1021/acs.jctc.1c00576},
urldate = {2022-08-22},
abstract = {We introduce a local machine-learning method for predicting the electron densities of periodic systems. The framework is based on a numerical, atom-centered auxiliary basis, which enables an accurate expansion of the all-electron density in a form suitable for learning isolated and periodic systems alike. We show that, using this formulation, the electron densities of metals, semiconductors, and molecular crystals can all be accurately predicted using symmetry-adapted Gaussian process regression models, properly adjusted for the nonorthogonal nature of the basis. These predicted densities enable the efficient calculation of electronic properties, which present errors on the order of tens of meV/atom when compared to ab initio density-functional calculations. We demonstrate the key power of this approach by using a model trained on ice unit cells containing only 4 water molecules to predict the electron densities of cells containing up to 512 molecules and see no increase in the magnitude of the errors of derived electronic properties when increasing the system size. Indeed, we find that these extrapolated derived energies are more accurate than those predicted using a direct machine-learning model. Finally, on heterogeneous data sets SALTED can predict electron densities with errors below 4\%.},
- keywords = {_tablet,DFT,GPR,lambda-SOAP,library,ML,ML-DFT,ML-ESM,models,molecules,molecules & solids,prediction of electron density,prediction of ground-state properties,Resolution of the identity,SA-GPR,SALTED,solids,with-code},
+ keywords = {\_tablet,DFT,GPR,lambda-SOAP,library,ML,ML-DFT,ML-ESM,models,molecules,molecules \& solids,prediction of electron density,prediction of ground-state properties,Resolution of the identity,SA-GPR,SALTED,solids,with-code},
file = {/Users/wasmer/Nextcloud/Zotero/Lewis et al_2021_Learning Electron Densities in the Condensed Phase.pdf;/Users/wasmer/Zotero/storage/S9FT2FEZ/acs.jctc.html}
}
+@online{lewisPredictingElectronicDensity2023,
+ title = {Predicting the {{Electronic Density Response}} of {{Condensed-Phase Systems}} to {{Electric Field Perturbations}}},
+ author = {Lewis, Alan M. and Lazzaroni, Paolo and Rossi, Mariana},
+ date = {2023-04-18},
+ eprint = {2304.09057},
+ eprinttype = {arxiv},
+ eprintclass = {cond-mat, physics:physics},
+ doi = {10.48550/arXiv.2304.09057},
+ url = {http://arxiv.org/abs/2304.09057},
+ urldate = {2023-05-26},
+ abstract = {We present a local and transferable machine learning approach capable of predicting the real-space density response of both molecules and periodic systems to external homogeneous electric fields. The new method, SALTER, builds on the Symmetry-Adapted Gaussian Process Regression SALTED framework. SALTER requires only a small, but necessary, modification to the descriptors used to represent the atomic environments. We present the performance of the method on isolated water molecules, bulk water and a naphthalene crystal. Root mean square errors of the predicted density response lie at or below 10\% with barely more than 100 training structures. Derived quantities, such as polarizability tensors and even Raman spectra further derived from these tensors show a good agreement with those calculated directly from quantum mechanical methods. Therefore, SALTER shows excellent performance when predicting derived quantities, while retaining all of the information contained in the full electronic response. This method is thus capable of learning vector fields in a chemical context and serves as a landmark for further developments.},
+ pubstate = {preprint},
+ keywords = {AML,Electric field,equivariant,GPR,lambda-SOAP,materials,ML,ML-DFT,ML-ESM,molecules,prediction of electron density,prediction of electron response,SA-GPR,SALTED,SOAP},
+ file = {/Users/wasmer/Nextcloud/Zotero/Lewis et al_2023_Predicting the Electronic Density Response of Condensed-Phase Systems to.pdf;/Users/wasmer/Zotero/storage/6CSD7WWL/2304.html}
+}
+
+@online{liaoEquiformerEquivariantGraph2023,
+ title = {Equiformer: {{Equivariant Graph Attention Transformer}} for {{3D Atomistic Graphs}}},
+ shorttitle = {Equiformer},
+ author = {Liao, Yi-Lun and Smidt, Tess},
+ date = {2023-02-27},
+ eprint = {2206.11990},
+ eprinttype = {arxiv},
+ eprintclass = {physics},
+ doi = {10.48550/arXiv.2206.11990},
+ url = {http://arxiv.org/abs/2206.11990},
+ urldate = {2023-05-26},
+ abstract = {Despite their widespread success in various domains, Transformer networks have yet to perform well across datasets in the domain of 3D atomistic graphs such as molecules even when 3D-related inductive biases like translational invariance and rotational equivariance are considered. In this paper, we demonstrate that Transformers can generalize well to 3D atomistic graphs and present Equiformer, a graph neural network leveraging the strength of Transformer architectures and incorporating SE(3)/E(3)-equivariant features based on irreducible representations (irreps). First, we propose a simple and effective architecture by only replacing original operations in Transformers with their equivariant counterparts and including tensor products. Using equivariant operations enables encoding equivariant information in channels of irreps features without complicating graph structures. With minimal modifications to Transformers, this architecture has already achieved strong empirical results. Second, we propose a novel attention mechanism called equivariant graph attention, which improves upon typical attention in Transformers through replacing dot product attention with multi-layer perceptron attention and including non-linear message passing. With these two innovations, Equiformer achieves competitive results to previous models on QM9, MD17 and OC20 datasets.},
+ pubstate = {preprint},
+ keywords = {AML,attention,E(3),e3nn,equivariant,GNN,graph attention,irreps,MD17,ML,MPNN,OC20,PyG,PyTorch,QM9,SE(3),transformer,with-code},
+ file = {/Users/wasmer/Nextcloud/Zotero/Liao_Smidt_2023_Equiformer.pdf;/Users/wasmer/Zotero/storage/33AF9I34/2206.html}
+}
+
+@article{liCriticalExaminationRobustness2023,
+ title = {A Critical Examination of Robustness and Generalizability of Machine Learning Prediction of Materials Properties},
+ author = {Li, Kangming and DeCost, Brian and Choudhary, Kamal and Greenwood, Michael and Hattrick-Simpers, Jason},
+ date = {2023-04-07},
+ journaltitle = {npj Computational Materials},
+ shortjournal = {npj Comput Mater},
+ volume = {9},
+ number = {1},
+ pages = {1--9},
+ publisher = {{Nature Publishing Group}},
+ issn = {2057-3960},
+ doi = {10.1038/s41524-023-01012-9},
+ url = {https://www.nature.com/articles/s41524-023-01012-9},
+ urldate = {2023-06-16},
+ abstract = {Recent advances in machine learning (ML) have led to substantial performance improvement in material database benchmarks, but an excellent benchmark score may not imply good generalization performance. Here we show that ML models trained on Materials Project 2018 can have severely degraded performance on new compounds in Materials Project 2021 due to the distribution shift. We discuss how to foresee the issue with a few simple tools. Firstly, the uniform manifold approximation and projection (UMAP) can be used to investigate the relation between the training and test data within the feature space. Secondly, the disagreement between multiple ML models on the test data can illuminate out-of-distribution samples. We demonstrate that the UMAP-guided and query by committee acquisition strategies can greatly improve prediction accuracy by adding only 1\% of the test data. We believe this work provides valuable insights for building databases and models that enable better robustness and generalizability.},
+ issue = {1},
+ langid = {english},
+ keywords = {\_tablet,ALIGNN,alloys,compositional descriptors,cross-validation,data imbalance,data scarcity,database generation,human bias,materials project,MP18,MP21,PCA,random forest,small data,train-test split,UMAP,unsupervised learning,with-code,with-data,XGB},
+ file = {/Users/wasmer/Nextcloud/Zotero/Li et al_2023_A critical examination of robustness and generalizability of machine learning.pdf}
+}
+
@article{liDeeplearningDensityFunctional2022,
title = {Deep-Learning Density Functional Theory {{Hamiltonian}} for Efficient Ab Initio Electronic-Structure Calculation},
author = {Li, He and Wang, Zun and Zou, Nianlong and Ye, Meng and Xu, Runzhang and Gong, Xiaoxun and Duan, Wenhui and Xu, Yong},
@@ -5494,10 +6728,31 @@
abstract = {The marriage of density functional theory (DFT) and deep-learning methods has the potential to revolutionize modern computational materials science. Here we develop a deep neural network approach to represent the DFT Hamiltonian (DeepH) of crystalline materials, aiming to bypass the computationally demanding self-consistent field iterations of DFT and substantially improve the efficiency of ab initio electronic-structure calculations. A general framework is proposed to deal with the large dimensionality and gauge (or rotation) covariance of the DFT Hamiltonian matrix by virtue of locality, and this is realized by a message-passing neural network for deep learning. High accuracy, high efficiency and good transferability of the DeepH method are generally demonstrated for various kinds of material system and physical property. The method provides a solution to the accuracy–efficiency dilemma of DFT and opens opportunities to explore large-scale material systems, as evidenced by a promising application in the study of twisted van der Waals materials.},
issue = {6},
langid = {english},
- keywords = {/unread,Computational methods,Electronic properties and materials,Electronic structure},
+ keywords = {\_tablet,2D material,AML,Berry phase,CNT,DeepH,defects,DFT,disordered,e3nn,equivariant,GGA,graphene,heterostructures,library,local coordinates,magnetism,materials,ML,ML-DFT,ML-ESM,MoS2,MPNN,near-sightedness,non-collinear,OpenMX,PBE,PCA,prediction of bandstructure,prediction of Berry phase,prediction of Hamiltonian matrix,SOC,spin-dependent,twisted bilayer graphene,vdW,vdW materials,with-code,with-data},
file = {/Users/wasmer/Nextcloud/Zotero/Li et al_2022_Deep-learning density functional theory Hamiltonian for efficient ab initio.pdf}
}
+@article{liDeeplearningElectronicstructureCalculation2023,
+ title = {Deep-Learning Electronic-Structure Calculation of Magnetic Superstructures},
+ author = {Li, He and Tang, Zechen and Gong, Xiaoxun and Zou, Nianlong and Duan, Wenhui and Xu, Yong},
+ date = {2023-04},
+ journaltitle = {Nature Computational Science},
+ shortjournal = {Nat Comput Sci},
+ volume = {3},
+ number = {4},
+ pages = {321--327},
+ publisher = {{Nature Publishing Group}},
+ issn = {2662-8457},
+ doi = {10.1038/s43588-023-00424-3},
+ url = {https://www.nature.com/articles/s43588-023-00424-3},
+ urldate = {2023-06-12},
+ abstract = {Ab initio studies of magnetic superstructures are indispensable to research on emergent quantum materials, but are currently bottlenecked by the formidable computational cost. Here, to break this bottleneck, we have developed a deep equivariant neural network framework to represent the density functional theory Hamiltonian of magnetic materials for efficient electronic-structure calculation. A neural network architecture incorporating a priori knowledge of fundamental physical principles, especially the nearsightedness principle and the equivariance requirements of Euclidean and time-reversal symmetries (\$\$E(3)\textbackslash times \textbackslash\{I,\{\{\{\textbackslash mathcal\{T\}\}\}\}\textbackslash\}\$\$), is designed, which is critical to capture the subtle magnetic effects. Systematic experiments on spin-spiral, nanotube and moiré magnets were performed, making the challenging study of magnetic skyrmions feasible.},
+ issue = {4},
+ langid = {english},
+ keywords = {\_tablet,AML,constrained DFT,DeepH,DFT,E(3),e3nn,ENN,equivariant,Heisenberg model,Jij,library,magnetic interactions,magnetic structure,magnetic supperlattice,magnetism,ML,ML-DFT,ML-ESM,MPNN,near-sightedness,OpenMX,PBE,prediction from magnetic configuration,prediction from structure,prediction of Hamiltonian matrix,prediction of Jij,prediction of magnetic moment,skyrmions,spin spiral,spin-dependent,transition metals,TRS,twisted bilayer,with-code,with-data},
+ file = {/Users/wasmer/Nextcloud/Zotero/Li et al_2023_Deep-learning electronic-structure calculation of magnetic superstructures.pdf}
+}
+
@unpublished{liDeepNeuralNetwork2021,
title = {Deep {{Neural Network Representation}} of {{Density Functional Theory Hamiltonian}}},
author = {Li, He and Wang, Zun and Zou, Nianlong and Ye, Meng and Duan, Wenhui and Xu, Yong},
@@ -5508,7 +6763,7 @@
url = {http://arxiv.org/abs/2104.03786},
urldate = {2022-01-02},
abstract = {The marriage of density functional theory (DFT) and deep learning methods has the potential to revolutionize modern research of material science. Here we study the crucial problem of representing DFT Hamiltonian for crystalline materials of arbitrary configurations via deep neural network. A general framework is proposed to deal with the infinite dimensionality and covariance transformation of DFT Hamiltonian matrix in virtue of locality and use message passing neural network together with graph representation for deep learning. Our example study on graphene-based systems demonstrates that high accuracy (\$\textbackslash sim\$meV) and good transferability can be obtained for DFT Hamiltonian, ensuring accurate predictions of materials properties without DFT. The Deep Hamiltonian method provides a solution to the accuracy-efficiency dilemma of DFT and opens new opportunities to explore large-scale materials and physics.},
- keywords = {Condensed Matter - Disordered Systems and Neural Networks,Condensed Matter - Materials Science,Condensed Matter - Mesoscale and Nanoscale Physics,Physics - Computational Physics,Quantum Physics},
+ keywords = {\_tablet,AML,DeepH,DFT,disordered,e3nn,equivariant,graphene,library,materials,ML,ML-DFT,ML-ESM,MPNN,OpenMX,original publication,PBE,prediction of Hamiltonian matrix,with-code},
file = {/Users/wasmer/Nextcloud/Zotero/Li et al_2021_Deep Neural Network Representation of Density Functional Theory Hamiltonian.pdf;/Users/wasmer/Zotero/storage/B7RUP7VH/2104.html}
}
@@ -5569,6 +6824,22 @@
file = {/Users/wasmer/Nextcloud/Zotero/Li et al_2014_The metallization and superconductivity of dense hydrogen sulfide.pdf}
}
+@online{limSignBasisInvariant2022,
+ title = {Sign and {{Basis Invariant Networks}} for {{Spectral Graph Representation Learning}}},
+ author = {Lim, Derek and Robinson, Joshua and Zhao, Lingxiao and Smidt, Tess and Sra, Suvrit and Maron, Haggai and Jegelka, Stefanie},
+ date = {2022-09-30},
+ eprint = {2202.13013},
+ eprinttype = {arxiv},
+ eprintclass = {cs, stat},
+ doi = {10.48550/arXiv.2202.13013},
+ url = {http://arxiv.org/abs/2202.13013},
+ urldate = {2023-05-26},
+ abstract = {We introduce SignNet and BasisNet -- new neural architectures that are invariant to two key symmetries displayed by eigenvectors: (i) sign flips, since if \$v\$ is an eigenvector then so is \$-v\$; and (ii) more general basis symmetries, which occur in higher dimensional eigenspaces with infinitely many choices of basis eigenvectors. We prove that under certain conditions our networks are universal, i.e., they can approximate any continuous function of eigenvectors with the desired invariances. When used with Laplacian eigenvectors, our networks are provably more expressive than existing spectral methods on graphs; for instance, they subsume all spectral graph convolutions, certain spectral graph invariants, and previously proposed graph positional encodings as special cases. Experiments show that our networks significantly outperform existing baselines on molecular graph regression, learning expressive graph representations, and learning neural fields on triangle meshes. Our code is available at https://github.com/cptq/SignNet-BasisNet .},
+ pubstate = {preprint},
+ keywords = {/unread,eigenvector,General ML,GNN,graph convolution,graph regression,library,ML,transformer,with-code},
+ file = {/Users/wasmer/Nextcloud/Zotero/Lim et al_2022_Sign and Basis Invariant Networks for Spectral Graph Representation Learning.pdf;/Users/wasmer/Zotero/storage/JA5U2K7L/2202.html}
+}
+
@article{lindmaaTheoreticalPredictionProperties2017,
title = {Theoretical Prediction of Properties of Atomistic Systems : {{Density}} Functional Theory and Machine Learning},
shorttitle = {Theoretical Prediction of Properties of Atomistic Systems},
@@ -5666,6 +6937,25 @@
file = {/Users/wasmer/Nextcloud/Zotero/Liu_Hesjedal_Magnetic Topological Insulator Heterostructures.pdf;/Users/wasmer/Zotero/storage/77XG2Q59/adma.html}
}
+@article{liuMagneticTopologicalInsulator2021,
+ title = {Magnetic {{Topological Insulator Heterostructures}}: {{A Review}}},
+ shorttitle = {Magnetic {{Topological Insulator Heterostructures}}},
+ author = {Liu, Jieyi and Hesjedal, Thorsten},
+ date = {2021-10-19},
+ journaltitle = {Advanced Materials},
+ volume = {n/a},
+ number = {n/a},
+ pages = {2102427},
+ issn = {1521-4095},
+ doi = {10.1002/adma.202102427},
+ url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202102427},
+ urldate = {2023-06-15},
+ abstract = {Topological insulators (TIs) provide intriguing prospects for the future of spintronics due to their large spin–orbit coupling and dissipationless, counter-propagating conduction channels in the surface state. The combination of topological properties and magnetic order can lead to new quantum states including the quantum anomalous Hall effect that was first experimentally realized in Cr-doped (Bi,Sb)2Te3 films. Since magnetic doping can introduce detrimental effects, requiring very low operational temperatures, alternative approaches are explored. Proximity coupling to magnetically ordered systems is an obvious option, with the prospect to raise the temperature for observing the various quantum effects. Here, an overview of proximity coupling and interfacial effects in TI heterostructures is presented, which provides a versatile materials platform for tuning the magnetic and topological properties of these exciting materials. An introduction is first given to the heterostructure growth by molecular beam epitaxy and suitable structural, electronic, and magnetic characterization techniques. Going beyond transition-metal-doped and undoped TI heterostructures, examples of heterostructures are discussed, including rare-earth-doped TIs, magnetic insulators, and antiferromagnets, which lead to exotic phenomena such as skyrmions and exchange bias. Finally, an outlook on novel heterostructures such as intrinsic magnetic TIs and systems including 2D materials is given.},
+ langid = {english},
+ keywords = {\_tablet,/unread,Hall effect,Hall QAHE,magnetic TIs,review,topological insulator},
+ file = {/Users/wasmer/Nextcloud/Zotero/Liu_Hesjedal_2021_Magnetic Topological Insulator Heterostructures.pdf;/Users/wasmer/Zotero/storage/VEP2MG97/adma.html}
+}
+
@article{liUnderstandingMachinelearnedDensity2016,
title = {Understanding Machine-Learned Density Functionals},
author = {Li, Li and Snyder, John C. and Pelaschier, Isabelle M. and Huang, Jessica and Niranjan, Uma-Naresh and Duncan, Paul and Rupp, Matthias and Müller, Klaus-Robert and Burke, Kieron},
@@ -5680,7 +6970,7 @@
urldate = {2022-07-08},
abstract = {Machine learning (ML) is an increasingly popular statistical tool for analyzing either measured or calculated data sets. Here, we explore its application to a well-defined physics problem, investigating issues of how the underlying physics is handled by ML, and how self-consistent solutions can be found by limiting the domain in which ML is applied. The particular problem is how to find accurate approximate density functionals for the kinetic energy (KE) of noninteracting electrons. Kernel ridge regression is used to approximate the KE of non-interacting fermions in a one dimensional box as a functional of their density. The properties of different kernels and methods of cross-validation are explored, reproducing the physics faithfully in some cases, but not others. We also address how self-consistency can be achieved with information on only a limited electronic density domain. Accurate constrained optimal densities are found via a modified Euler-Lagrange constrained minimization of the machine-learned total energy, despite the poor quality of its functional derivative. A projected gradient descent algorithm is derived using local principal component analysis. Additionally, a sparse grid representation of the density can be used without degrading the performance of the methods. The implications for machine-learned density functional approximations are discussed. © 2015 Wiley Periodicals, Inc.},
langid = {english},
- keywords = {_tablet,DFA,DFT,KRR,ML,ML-DFA,ML-DFT,ML-ESM,tutorial},
+ keywords = {\_tablet,DFA,DFT,KRR,ML,ML-DFA,ML-DFT,ML-ESM,tutorial},
file = {/Users/wasmer/Nextcloud/Zotero/Li et al_2016_Understanding machine-learned density functionals.pdf;/Users/wasmer/Zotero/storage/ZPNDJ7AU/qua.html}
}
@@ -5755,6 +7045,24 @@
file = {/Users/wasmer/Nextcloud/Zotero/Long et al_2022_Inverse design of crystal structures for multicomponent systems.pdf;/Users/wasmer/Zotero/storage/6PDR73FE/S135964542200283X.html}
}
+@article{lopanitsynaModelingHighentropyTransition2023,
+ title = {Modeling High-Entropy Transition Metal Alloys with Alchemical Compression},
+ author = {Lopanitsyna, Nataliya and Fraux, Guillaume and Springer, Maximilian A. and De, Sandip and Ceriotti, Michele},
+ date = {2023-04-26},
+ journaltitle = {Physical Review Materials},
+ shortjournal = {Phys. Rev. Mater.},
+ volume = {7},
+ number = {4},
+ pages = {045802},
+ publisher = {{American Physical Society}},
+ doi = {10.1103/PhysRevMaterials.7.045802},
+ url = {https://link.aps.org/doi/10.1103/PhysRevMaterials.7.045802},
+ urldate = {2023-05-05},
+ abstract = {Alloys composed of several elements in roughly equimolar composition, often referred to as high-entropy alloys, have long been of interest for their thermodynamics and peculiar mechanical properties, and more recently for their potential application in catalysis. They are a considerable challenge to traditional atomistic modeling, and also to data-driven potentials that for the most part have memory footprint, computational effort, and data requirements which scale poorly with the number of elements included. We apply a recently proposed scheme to compress chemical information in a lower-dimensional space, which reduces dramatically the cost of the model with negligible loss of accuracy, to build a potential that can describe 25 d-block transition metals. The model shows semiquantitative accuracy for prototypical alloys and is remarkably stable when extrapolating to structures outside its training set. We use this framework to study element segregation in a computational experiment that simulates an equimolar alloy of all 25 elements, mimicking the seminal experiments in the groups of Yeh and Cantor, and use our observations on the short-range order relations between the elements to define a data-driven set of Hume-Rothery rules that can serve as guidance for alloy design. We conclude with a study of three prototypical alloys, CoCrFeMnNi, CoCrFeMoNi, and IrPdPtRhRu, determining their stability and the short-range order behavior of their constituents.},
+ keywords = {\_tablet,ACE,alchemical,chemical species scaling problem,descriptors,dimensionality reduction,high-entropy alloys,library,MTP,PyTorch,SOAP,transition metals,with-code},
+ file = {/Users/wasmer/Nextcloud/Zotero/false;/Users/wasmer/Nextcloud/Zotero/Lopanitsyna et al_2023_Modeling high-entropy transition metal alloys with alchemical compression.pdf;/Users/wasmer/Zotero/storage/WWWS3KMP/PhysRevMaterials.7.html}
+}
+
@online{lopanitsynaModelingHighentropyTransitionmetal2022,
title = {Modeling High-Entropy Transition-Metal Alloys with Alchemical Compression},
author = {Lopanitsyna, Nataliya and Fraux, Guillaume and Springer, Maximilian A. and De, Sandip and Ceriotti, Michele},
@@ -5767,7 +7075,7 @@
urldate = {2022-12-29},
abstract = {Alloys composed of several elements in roughly equimolar composition, often referred to as high-entropy alloys, have long been of interest for their thermodynamics and peculiar mechanical properties, and more recently for their potential application in catalysis. They are a considerable challenge to traditional atomistic modeling, and also to data-driven potentials that for the most part have memory footprint, computational effort and data requirements which scale poorly with the number of elements included. We apply a recently proposed scheme to compress chemical information in a lower-dimensional space, which reduces dramatically the cost of the model with negligible loss of accuracy, to build a potential that can describe 25 d-block transition metals. The model shows semi-quantitative accuracy for prototypical alloys, and is remarkably stable when extrapolating to structures outside its training set. We use this framework to study element segregation in a computational experiment that simulates an equimolar alloy of all 25 elements, mimicking the seminal experiments by Cantor et al., and use our observations on the short-range order relations between the elements to define a data-driven set of Hume-Rothery rules that can serve as guidance for alloy design. We conclude with a study of three prototypical alloys, CoCrFeMnNi, CoCrFeMoNi and IrPdPtRhRu, determining their stability and the short-range order behavior of their constituents.},
pubstate = {preprint},
- keywords = {_tablet,ACE,alchemical,chemical species scaling problem,descriptors,dimensionality reduction,high-entropy alloys,MTP,PyTorch,SOAP,transition metals,with-code},
+ keywords = {\_tablet,ACE,alchemical,chemical species scaling problem,descriptors,dimensionality reduction,high-entropy alloys,MTP,PyTorch,SOAP,transition metals,with-code},
file = {/Users/wasmer/Nextcloud/Zotero/Lopanitsyna et al_2022_Modeling high-entropy transition-metal alloys with alchemical compression.pdf;/Users/wasmer/Nextcloud/Zotero/Lopanitsyna et al_2022_Modeling high-entropy transition-metal alloys with alchemical compression2.pdf;/Users/wasmer/Zotero/storage/QNGQ9AQD/2212.html}
}
@@ -5787,6 +7095,26 @@
file = {/Users/wasmer/Nextcloud/Zotero/Lounis_2007_Theory of Magnetic Transition Metal Nanoclusters on Surfaces.pdf}
}
+@article{luhBOUNDSTATESUPERCONDUCTORS1965,
+ title = {BOUND STATE IN SUPERCONDUCTORS WITH PARAMAGNETIC IMPURITIES},
+ author = {Luh, Yu},
+ date = {1965-01-01},
+ journaltitle = {物理学报},
+ shortjournal = {Acta Phys. Sin.},
+ volume = {21},
+ number = {1},
+ pages = {75--91},
+ publisher = {{物理学报}},
+ issn = {1000-3290},
+ doi = {10.7498/aps.21.75},
+ url = {https://wulixb.iphy.ac.cn/en/article/doi/10.7498/aps.21.75},
+ urldate = {2023-05-10},
+ abstract = {A generalized canonical transformation and a SCF method have been used to investigate the influence of isolated impurity atoms on the properties of superconductors. It has been found that a bound state of excitation exists around a paramagnetic impurity with its energy level in the energy gap. An analytical expression has been obtained for the corresponding wave function. The effect of electromagnetic absorption due to the bound state should appear as a precursory peak. The possible experimental verifications of the bound state through tunnelling effect and infrared absorption are discussed.Futhermore, the excitations of continuous spectra around a nonmagnetic impurity and the spatial variation of the energy gap parameter have been considered.},
+ langid = {cn},
+ keywords = {impurity embedding,magnetic impurity,magnetism,original publication,paramagnetic impurity,physics,rec-by-da-silva,superconductor,Yu-Shiba-Rusinov,Yu-Shiba-Rusinov state},
+ file = {/Users/wasmer/Nextcloud/Zotero/Luh_2005_BOUND STATE IN SUPERCONDUCTORS WITH PARAMAGNETIC IMPURITIES.pdf}
+}
+
@article{lunghiComputationalDesignMagnetic2022,
title = {Computational Design of Magnetic Molecules and Their Environment Using Quantum Chemistry, Machine Learning and Multiscale Simulations},
author = {Lunghi, Alessandro and Sanvito, Stefano},
@@ -5821,7 +7149,7 @@
urldate = {2023-02-23},
abstract = {The advent of computational statistical disciplines, such as machine learning, is leading to a paradigm shift in the way we conceive the design of new compounds. Today computational science does not only provide a sound understanding of experiments, but also can directly design the best compound for specific applications. This approach, known as reverse engineering, requires the construction of models able to efficiently predict continuous structure-property maps. Here we show that reverse engineering can be used to tune the magnetic properties of a single-ion molecular magnet in an automated intelligent fashion. We design a machine learning model to predict both the energy and magnetic properties as function of the chemical structure. Then, a particle-swarm optimization algorithm is used to explore the conformational landscapes in the search for new molecular structures leading to an enhanced magnetic anisotropy. We find that a 5\% change in one of the coordination angles leads to a 50\% increase in the anisotropy. Our approach paves the way for a machine-learning-driven exploration of the chemical space of general classes of magnetic materials. Most importantly, it can be applied to any structure-property relation and offers an effective way to automatically generate new materials with target properties starting from the knowledge of previously synthesized ones.},
pubstate = {preprint},
- keywords = {_tablet,/unread,Condensed Matter - Materials Science,Physics - Computational Physics},
+ keywords = {\_tablet,/unread,Condensed Matter - Materials Science,Physics - Computational Physics},
file = {/Users/wasmer/Nextcloud/Zotero/Lunghi_Sanvito_2019_Surfing multiple conformation-property landscapes via machine learning.pdf;/Users/wasmer/Zotero/storage/FQSQYUBP/1911.html}
}
@@ -5858,10 +7186,42 @@
abstract = {The atomic cluster expansion is a general polynomial expansion of the atomic energy in multi-atom basis functions. Here we implement the atomic cluster expansion in the performant C++ code PACE that is suitable for use in large-scale atomistic simulations. We briefly review the atomic cluster expansion and give detailed expressions for energies and forces as well as efficient algorithms for their evaluation. We demonstrate that the atomic cluster expansion as implemented in PACE shifts a previously established Pareto front for machine learning interatomic potentials toward faster and more accurate calculations. Moreover, general purpose parameterizations are presented for copper and silicon and evaluated in detail. We show that the Cu and Si potentials significantly improve on the best available potentials for highly accurate large-scale atomistic simulations.},
issue = {1},
langid = {english},
- keywords = {_tablet,ACE,C++,descriptors,library},
+ keywords = {\_tablet,ACE,C++,descriptors,library},
file = {/Users/wasmer/Nextcloud/Zotero/Lysogorskiy et al_2021_Performant implementation of the atomic cluster expansion (PACE) and.pdf;/Users/wasmer/Zotero/storage/QVQD97QT/s41524-021-00559-9.html}
}
+@article{m.tealeDFTExchangeSharing2022,
+ title = {{{DFT}} Exchange: Sharing Perspectives on the Workhorse of Quantum Chemistry and Materials Science},
+ shorttitle = {{{DFT}} Exchange},
+ author = {M.~Teale, Andrew and Helgaker, Trygve and Savin, Andreas and Adamo, Carlo and Aradi, Bálint and V.~Arbuznikov, Alexei and W.~Ayers, Paul and Jan~Baerends, Evert and Barone, Vincenzo and Calaminici, Patrizia and Cancès, Eric and A.~Carter, Emily and Kumar~Chattaraj, Pratim and Chermette, Henry and Ciofini, Ilaria and Daniel~Crawford, T. and Proft, Frank De and F.~Dobson, John and Draxl, Claudia and Frauenheim, Thomas and Fromager, Emmanuel and Fuentealba, Patricio and Gagliardi, Laura and Galli, Giulia and Gao, Jiali and Geerlings, Paul and Gidopoulos, Nikitas and W.~Gill, Peter M. and Gori-Giorgi, Paola and Görling, Andreas and Gould, Tim and Grimme, Stefan and Gritsenko, Oleg and Aagaard~Jensen, Hans Jørgen and R.~Johnson, Erin and O.~Jones, Robert and Kaupp, Martin and M.~Köster, Andreas and Kronik, Leeor and I.~Krylov, Anna and Kvaal, Simen and Laestadius, Andre and Levy, Mel and Lewin, Mathieu and Liu, Shubin and Loos, Pierre-François and T.~Maitra, Neepa and Neese, Frank and P.~Perdew, John and Pernal, Katarzyna and Pernot, Pascal and Piecuch, Piotr and Rebolini, Elisa and Reining, Lucia and Romaniello, Pina and Ruzsinszky, Adrienn and R.~Salahub, Dennis and Scheffler, Matthias and Schwerdtfeger, Peter and N.~Staroverov, Viktor and Sun, Jianwei and Tellgren, Erik and J.~Tozer, David and B.~Trickey, Samuel and A.~Ullrich, Carsten and Vela, Alberto and Vignale, Giovanni and A.~Wesolowski, Tomasz and Xu, Xin and Yang, Weitao},
+ date = {2022},
+ journaltitle = {Physical Chemistry Chemical Physics},
+ volume = {24},
+ number = {47},
+ pages = {28700--28781},
+ publisher = {{Royal Society of Chemistry}},
+ doi = {10.1039/D2CP02827A},
+ url = {https://pubs.rsc.org/en/content/articlelanding/2022/cp/d2cp02827a},
+ urldate = {2023-06-30},
+ langid = {english},
+ keywords = {DFT,DFT exchange,Interviews,perspective,xc functional},
+ file = {/Users/wasmer/Nextcloud/Zotero/M. Teale et al_2022_DFT exchange.pdf}
+}
+
+@online{maffettoneWhatMissingAutonomous2023,
+ title = {What Is Missing in Autonomous Discovery: {{Open}} Challenges for the Community},
+ shorttitle = {What Is Missing in Autonomous Discovery},
+ author = {Maffettone, Phillip M. and Friederich, Pascal and Baird, Sterling G. and Blaiszik, Ben and Brown, Keith A. and Campbell, Stuart I. and Cohen, Orion A. and Collins, Tantum and Davis, Rebecca L. and Foster, Ian T. and Haghmoradi, Navid and Hereld, Mark and Jung, Nicole and Kwon, Ha-Kyung and Pizzuto, Gabriella and Rintamaki, Jacob and Steinmann, Casper and Torresi, Luca and Sun, Shijing},
+ date = {2023-04-21},
+ url = {https://arxiv.org/abs/2304.11120v2},
+ urldate = {2023-07-01},
+ abstract = {Self-driving labs (SDLs) leverage combinations of artificial intelligence, automation, and advanced computing to accelerate scientific discovery. The promise of this field has given rise to a rich community of passionate scientists, engineers, and social scientists, as evidenced by the development of the Acceleration Consortium and recent Accelerate Conference. Despite its strengths, this rapidly developing field presents numerous opportunities for growth, challenges to overcome, and potential risks of which to remain aware. This community perspective builds on a discourse instantiated during the first Accelerate Conference, and looks to the future of self-driving labs with a tempered optimism. Incorporating input from academia, government, and industry, we briefly describe the current status of self-driving labs, then turn our attention to barriers, opportunities, and a vision for what is possible. Our field is delivering solutions in technology and infrastructure, artificial intelligence and knowledge generation, and education and workforce development. In the spirit of community, we intend for this work to foster discussion and drive best practices as our field grows.},
+ langid = {english},
+ organization = {{arXiv.org}},
+ keywords = {/unread},
+ file = {/Users/wasmer/Nextcloud/Zotero/Maffettone et al_2023_What is missing in autonomous discovery.pdf}
+}
+
@article{magalhaesDensityFunctionalTheory2017,
title = {Density {{Functional Theory Calculation}} of the {{Absorption Properties}} of {{Brown Carbon Chromophores Generated}} by {{Catechol Heterogeneous Ozonolysis}}},
author = {Magalhães, Ana Catarina O. and Esteves da Silva, Joaquim C. G. and Pinto da Silva, Luís},
@@ -5904,6 +7264,22 @@
file = {/Users/wasmer/Nextcloud/Zotero/Majlis_2007_The Quantum Theory of Magnetism.pdf}
}
+@online{mandalTopologicalSuperconductorsMaterials2023,
+ title = {Topological Superconductors from a Materials Perspective},
+ author = {Mandal, Manasi and Drucker, Nathan C. and Siriviboon, Phum and Nguyen, Thanh and Boonkird, Tiya and Lamichhane, Tej Nath and Okabe, Ryotaro and Chotrattanapituk, Abhijatmedhi and Li, Mingda},
+ date = {2023-03-27},
+ eprint = {2303.15581},
+ eprinttype = {arxiv},
+ eprintclass = {cond-mat},
+ doi = {10.48550/arXiv.2303.15581},
+ url = {http://arxiv.org/abs/2303.15581},
+ urldate = {2023-06-12},
+ abstract = {Topological superconductors (TSCs) have garnered significant research and industry attention in the past two decades. By hosting Majorana bound states which can be used as qubits that are robust against local perturbations, TSCs offer a promising platform toward (non-universal) topological quantum computation. However, there has been a scarcity of TSC candidates, and the experimental signatures that identify a TSC are often elusive. In this perspective, after a short review of the TSC basics and theories, we provide an overview of the TSC materials candidates, including natural compounds and synthetic material systems. We further introduce various experimental techniques to probe TSC, focusing on how a system is identified as a TSC candidate, and why a conclusive answer is often challenging to draw. We conclude by calling for new experimental signatures and stronger computational support to accelerate the search for new TSC candidates.},
+ pubstate = {preprint},
+ keywords = {/unread,materials,perspective,physics,superconductor,Topological Superconductor},
+ file = {/Users/wasmer/Zotero/storage/4J3A87J3/Mandal et al. - 2023 - Topological superconductors from a materials persp.pdf;/Users/wasmer/Zotero/storage/L8FFWWRW/2303.html}
+}
+
@article{manzoorMachineLearningBased2021,
title = {Machine {{Learning Based Methodology}} to {{Predict Point Defect Energies}} in {{Multi-Principal Element Alloys}}},
author = {Manzoor, Anus and Arora, Gaurav and Jerome, Bryant and Linton, Nathan and Norman, Bailey and Aidhy, Dilpuneet S.},
@@ -5953,7 +7329,7 @@
urldate = {2023-03-20},
abstract = {Machine learning (ML) algorithms are currently emerging as powerful tools in all areas of science. Conventionally, ML is understood as a fundamentally data-driven endeavour. Unfortunately, large well-curated databases are sparse in chemistry. In this contribution, I therefore review science-driven ML approaches which do not rely on “big data”, focusing on the atomistic modelling of materials and molecules. In this context, the term science-driven refers to approaches that begin with a scientific question and then ask what training data and model design choices are appropriate. As key features of science-driven ML, the automated and purpose-driven collection of data and the use of chemical and physical priors to achieve high data-efficiency are discussed. Furthermore, the importance of appropriate model evaluation and error estimation is emphasized.},
langid = {english},
- keywords = {_tablet,active learning,all-electron,AML,body-order,data-driven,database generation,delta learning,equivariant,inductive bias,iterative learning,iterative learning scheme,MACE,ML,ML-DFA,ML-DFT,ML-ESM,model evaluation,physical prior,physics-informed ML,prediction of electron density,review,review-of-AML,science-driven,uncertainty quantification},
+ keywords = {\_tablet,active learning,all-electron,AML,body-order,data-driven,database generation,delta learning,equivariant,inductive bias,iterative learning,iterative learning scheme,MACE,ML,ML-DFA,ML-DFT,ML-ESM,model evaluation,physical prior,physics-informed ML,prediction of electron density,review,review-of-AML,science-driven,uncertainty quantification},
file = {/Users/wasmer/Zotero/storage/LIPPS6I7/Margraf_2023_Science-Driven Atomistic Machine Learning.pdf;/Users/wasmer/Zotero/storage/V3VTFITJ/ange.html}
}
@@ -5996,12 +7372,12 @@
pages = {131--158},
publisher = {{NIC-Secretariat, Research Centre Jülich}},
location = {{Jülich}},
- url = {https://juser.fz-juelich.de/record/50027/files/FZJ-2014-02214.pdf},
+ url = {http://hdl.handle.net/2128/4777},
urldate = {2021-06-28},
abstract = {The Korringa-Kohn-Rostoker (KKR) method for the calculation of the electronic structure ofmaterials is founded on the concepts of the Green function and of multiple-scattering. In thismanuscript, after a short introduction to Green functions,we present a description of single-site scattering (including anisotropic potentials) and multiple-scattering theory and the KKRequations. The KKR representation of the Green function andthe algebraic Dyson equation areintroduced. We then discuss the screened KKR formalism, andits advantages in the numericaleffort for the calculation of layered systems. Finally we give a summary of the self-consistencyalgorithm for the calculation of the electronic structure.},
eventtitle = {Computational {{Nanoscience}}: {{Do It Yourself}}!},
isbn = {3-00-017350-1},
- keywords = {_tablet,FZJ,KKR,PGI-1/IAS-1},
+ keywords = {\_tablet,FZJ,KKR,PGI-1/IAS-1},
annotation = {Johannes Grotendorst, Stefan Blügel, Dominik Marx (Editors)},
file = {/Users/wasmer/Nextcloud/Zotero/Mavropoulos_Papanikolaou_2006_The Korringa-Kohn-Rostoker (KKR) Green function method I.pdf}
}
@@ -6096,27 +7472,8 @@
urldate = {2022-10-17},
abstract = {The determination of magnetic structure poses a long-standing challenge in condensed matter physics and materials science. Experimental techniques such as neutron diffraction are resource-limited and require complex structure refinement protocols, while computational approaches such as first-principles density functional theory (DFT) need additional semi-empirical correction, and reliable prediction is still largely limited to collinear magnetism. Here, we present a machine learning model that aims to classify the magnetic structure by inputting atomic coordinates containing transition metal and rare earth elements. By building a Euclidean equivariant neural network that preserves the crystallographic symmetry, the magnetic structure (ferromagnetic, antiferromagnetic, and non-magnetic) and magnetic propagation vector (zero or non-zero) can be predicted with an average accuracy of 77.8\% and 73.6\%. In particular, a 91\% accuracy is reached when predicting no magnetic ordering even if the structure contains magnetic element(s). Our work represents one step forward to solving the grand challenge of full magnetic structure determination.},
langid = {english},
- keywords = {_tablet,classification,e3nn,equivariant,magnetic moment,magnetic order,magnetism,ML},
- file = {/Users/wasmer/Nextcloud/Zotero/Merker et al_2022_Machine learning magnetism classifiers from atomic coordinates.pdf;/Users/wasmer/Zotero/storage/7UQX89UL/S258900422201464X.html}
-}
-
-@article{merkerMachineLearningMagnetism2022a,
- title = {Machine Learning Magnetism Classifiers from Atomic Coordinates},
- author = {Merker, Helena A. and Heiberger, Harry and Nguyen, Linh and Liu, Tongtong and Chen, Zhantao and Andrejevic, Nina and Drucker, Nathan C. and Okabe, Ryotaro and Kim, Song Eun and Wang, Yao and Smidt, Tess and Li, Mingda},
- date = {2022-10-21},
- journaltitle = {iScience},
- shortjournal = {iScience},
- volume = {25},
- number = {10},
- pages = {105192},
- issn = {2589-0042},
- doi = {10.1016/j.isci.2022.105192},
- url = {https://www.sciencedirect.com/science/article/pii/S258900422201464X},
- urldate = {2023-04-03},
- abstract = {The determination of magnetic structure poses a long-standing challenge in condensed matter physics and materials science. Experimental techniques such as neutron diffraction are resource-limited and require complex structure refinement protocols, while computational approaches such as first-principles density functional theory (DFT) need additional semi-empirical correction, and reliable prediction is still largely limited to collinear magnetism. Here, we present a machine learning model that aims to classify the magnetic structure by inputting atomic coordinates containing transition metal and rare earth elements. By building a Euclidean equivariant neural network that preserves the crystallographic symmetry, the magnetic structure (ferromagnetic, antiferromagnetic, and non-magnetic) and magnetic propagation vector (zero or non-zero) can be predicted with an average accuracy of 77.8\% and 73.6\%. In particular, a 91\% accuracy is reached when predicting no magnetic ordering even if the structure contains magnetic element(s). Our work represents one step forward to solving the grand challenge of full magnetic structure determination.},
- langid = {english},
- keywords = {AFM,AML,classification,classification of magnetic structure,collinear,e3nn,electronegativity,equivariant,FM,GNN,MAGNDATA,magnetic order,magnetic structure,magnetism,magnetism database,materials,materials project,ML,MPNN,non-collinear,polarizability,prediction of magnetic order,propagation vector,rare earths,spin-dependent,transition metals,vectorial learning target,with-code},
- file = {/Users/wasmer/Nextcloud/Zotero/Merker et al_2022_Machine learning magnetism classifiers from atomic coordinates2.pdf;/Users/wasmer/Zotero/storage/7YNIAY2A/S258900422201464X.html}
+ keywords = {\_tablet,AFM,AML,classification,classification of magnetic structure,collinear,e3nn,electronegativity,equivariant,FM,GNN,library,MAGNDATA,magnetic moment,magnetic order,magnetic structure,magnetism,magnetism database,materials,materials project,ML,MPNN,non-collinear,polarizability,prediction of magnetic order,propagation vector,rare earths,spin-dependent,transition metals,vectorial learning target,with-code},
+ file = {/Users/wasmer/Nextcloud/Zotero/false;/Users/wasmer/Nextcloud/Zotero/Merker et al_2022_Machine learning magnetism classifiers from atomic coordinates.pdf;/Users/wasmer/Zotero/storage/7UQX89UL/S258900422201464X.html}
}
@article{merkysPosterioriMetadataAutomated2017,
@@ -6138,6 +7495,24 @@
file = {/Users/wasmer/Nextcloud/Zotero/Merkys et al_2017_A posteriori metadata from automated provenance tracking.pdf;/Users/wasmer/Zotero/storage/9ZIMVPJ8/s13321-017-0242-y.html}
}
+@article{microsoftquantumInAsAlHybridDevices2023,
+ title = {{{InAs-Al}} Hybrid Devices Passing the Topological Gap Protocol},
+ author = {{Microsoft Quantum} and Aghaee, Morteza and Akkala, Arun and Alam, Zulfi and Ali, Rizwan and Alcaraz Ramirez, Alejandro and Andrzejczuk, Mariusz and Antipov, Andrey E. and Aseev, Pavel and Astafev, Mikhail and Bauer, Bela and Becker, Jonathan and Boddapati, Srini and Boekhout, Frenk and Bommer, Jouri and Bosma, Tom and Bourdet, Leo and Boutin, Samuel and Caroff, Philippe and Casparis, Lucas and Cassidy, Maja and Chatoor, Sohail and Christensen, Anna Wulf and Clay, Noah and Cole, William S. and Corsetti, Fabiano and Cui, Ajuan and Dalampiras, Paschalis and Dokania, Anand and family=Lange, given=Gijs, prefix=de, useprefix=true and family=Moor, given=Michiel, prefix=de, useprefix=true and Estrada Saldaña, Juan Carlos and Fallahi, Saeed and Fathabad, Zahra Heidarnia and Gamble, John and Gardner, Geoff and Govender, Deshan and Griggio, Flavio and Grigoryan, Ruben and Gronin, Sergei and Gukelberger, Jan and Hansen, Esben Bork and Heedt, Sebastian and Herranz Zamorano, Jesús and Ho, Samantha and Holgaard, Ulrik Laurens and Ingerslev, Henrik and Johansson, Linda and Jones, Jeffrey and Kallaher, Ray and Karimi, Farhad and Karzig, Torsten and King, Cameron and Kloster, Maren Elisabeth and Knapp, Christina and Kocon, Dariusz and Koski, Jonne and Kostamo, Pasi and Krogstrup, Peter and Kumar, Mahesh and Laeven, Tom and Larsen, Thorvald and Li, Kongyi and Lindemann, Tyler and Love, Julie and Lutchyn, Roman and Madsen, Morten Hannibal and Manfra, Michael and Markussen, Signe and Martinez, Esteban and McNeil, Robert and Memisevic, Elvedin and Morgan, Trevor and Mullally, Andrew and Nayak, Chetan and Nielsen, Jens and Nielsen, William Hvidtfelt Padkær and Nijholt, Bas and Nurmohamed, Anne and O'Farrell, Eoin and Otani, Keita and Pauka, Sebastian and Petersson, Karl and Petit, Luca and Pikulin, Dmitry I. and Preiss, Frank and Quintero-Perez, Marina and Rajpalke, Mohana and Rasmussen, Katrine and Razmadze, Davydas and Reentila, Outi and Reilly, David and Rouse, Richard and Sadovskyy, Ivan and Sainiemi, Lauri and Schreppler, Sydney and Sidorkin, Vadim and Singh, Amrita and Singh, Shilpi and Sinha, Sarat and Sohr, Patrick and Stankevič, Tomaš and Stek, Lieuwe and Suominen, Henri and Suter, Judith and Svidenko, Vicky and Teicher, Sam and Temuerhan, Mine and Thiyagarajah, Nivetha and Tholapi, Raj and Thomas, Mason and Toomey, Emily and Upadhyay, Shivendra and Urban, Ivan and Vaitiekėnas, Saulius and Van Hoogdalem, Kevin and Van Woerkom, David and Viazmitinov, Dmitrii V. and Vogel, Dominik and Waddy, Steven and Watson, John and Weston, Joseph and Winkler, Georg W. and Yang, Chung Kai and Yau, Sean and Yi, Daniel and Yucelen, Emrah and Webster, Alex and Zeisel, Roland and Zhao, Ruichen},
+ date = {2023-06-21},
+ journaltitle = {Physical Review B},
+ shortjournal = {Phys. Rev. B},
+ volume = {107},
+ number = {24},
+ pages = {245423},
+ publisher = {{American Physical Society}},
+ doi = {10.1103/PhysRevB.107.245423},
+ url = {https://link.aps.org/doi/10.1103/PhysRevB.107.245423},
+ urldate = {2023-06-26},
+ abstract = {We present measurements and simulations of semiconductor-superconductor heterostructure devices that are consistent with the observation of topological superconductivity and Majorana zero modes. The devices are fabricated from high-mobility two-dimensional electron gases in which quasi-one-dimensional wires are defined by electrostatic gates. These devices enable measurements of local and nonlocal transport properties and have been optimized via extensive simulations to ensure robustness against nonuniformity and disorder. Our main result is that several devices, fabricated according to the design's engineering specifications, have passed the topological gap protocol defined in Pikulin et al. (arXiv:2103.12217). This protocol is a stringent test composed of a sequence of three-terminal local and nonlocal transport measurements performed while varying the magnetic field, semiconductor electron density, and junction transparencies. Passing the protocol indicates a high probability of detection of a topological phase hosting Majorana zero modes as determined by large-scale disorder simulations. Our experimental results are consistent with a quantum phase transition into a topological superconducting phase that extends over several hundred millitesla in magnetic field and several millivolts in gate voltage, corresponding to approximately one hundred microelectronvolts in Zeeman energy and chemical potential in the semiconducting wire. These regions feature a closing and reopening of the bulk gap, with simultaneous zero-bias conductance peaks at both ends of the devices that withstand changes in the junction transparencies. The extracted maximum topological gaps in our devices are 20–60µeV. This demonstration is a prerequisite for experiments involving fusion and braiding of Majorana zero modes.},
+ keywords = {ARPES,device engineering,electrostatics simulation,experimental science,Majorana,mesoscopic,Microsoft Research,MZM,physics,quantum computing,topological gap,topological insulator,topological invariant,transport properties,transport simulation},
+ file = {/Users/wasmer/Nextcloud/Zotero/Microsoft Quantum et al_2023_InAs-Al hybrid devices passing the topological gap protocol.pdf;/Users/wasmer/Zotero/storage/R35YJAWP/PhysRevB.107.html}
+}
+
@online{minotakisMachineLearningSurrogateModel2023,
title = {Machine-{{Learning Surrogate Model}} for {{Accelerating}} the {{Search}} of {{Stable Ternary Alloys}}},
author = {Minotakis, Michael and Rossignol, Hugo and Cobelli, Matteo and Sanvito, Stefano},
@@ -6150,7 +7525,7 @@
urldate = {2023-04-13},
abstract = {The prediction of phase diagrams in the search for new phases is a complex and computationally intensive task. Density functional theory provides, in many situations, the desired accuracy, but its throughput becomes prohibitively limited as the number of species involved grows, even when used with local and semi-local functionals. Here, we explore the possibility of integrating machine-learning models in the workflow for the construction of ternary convex hull diagrams. In particular, we train a set of spectral neighbour-analysis potentials (SNAPs) over readily available binary phases and we establish whether this is good enough to predict the energies of novel ternaries. Such a strategy does not require any new calculations specific for the construction of the model, but just avails of data stored in binary-phase-diagram repositories. We find that a so-constructed SNAP is capable of accurate total-energy estimates for ternary phases close to the equilibrium geometry but, in general, is not able to perform atomic relaxation. This is because during a typical relaxation path a given phase traverses regions in the parameter space poorly represented by the training set. Different metrics are then investigated to assess how an unknown structure is well described by a given SNAP model, and we find that the standard deviation of an ensemble of SNAPs provides a fast and non-specie-specific metric.},
pubstate = {preprint},
- keywords = {AFLOW,AFLOWLIB,AML,binary systems,convex hull,High-throughput,LAMMPS,materials discovery,materials screening,ML,MLP,MTP,PCA,phase diagram,prediction of energy,scikit-learn,SNAP,structure relaxation,ternary systems,VASP},
+ keywords = {\_tablet,AFLOW,AFLOWLIB,AML,binary systems,convex hull,High-throughput,LAMMPS,materials discovery,materials screening,ML,MLP,MTP,PCA,phase diagram,prediction of energy,scikit-learn,SNAP,structure relaxation,ternary systems,VASP},
file = {/Users/wasmer/Nextcloud/Zotero/Minotakis et al_2023_Machine-Learning Surrogate Model for Accelerating the Search of Stable Ternary.pdf;/Users/wasmer/Zotero/storage/L8VGVV3E/2303.html}
}
@@ -6203,7 +7578,7 @@
url = {https://doi.org/10.1021/acs.jpclett.2c03670},
urldate = {2023-04-04},
abstract = {We present an analysis of the static exchange-correlation (XC) kernel computed from hybrid functionals with a single mixing coefficient such as PBE0 and PBE0–1/3. We break down the hybrid XC kernels into the exchange and correlation parts using the Hartree–Fock functional, the exchange-only PBE, and the correlation-only PBE. This decomposition is combined with exact data for the static XC kernel of the uniform electron gas and an Airy gas model within a subsystem functional approach. This gives us a tool for the non-empirical choice of the mixing coefficient under ambient and extreme conditions. Our analysis provides physical insights into the effect of the variation of the mixing coefficient in hybrid functionals, which is of immense practical value. The presented approach is general and can be used for other types of functionals like screened hybrids.},
- keywords = {/unread,CASUS,DFA,DFT,HZDR,PGI-1/IAS-1},
+ keywords = {\_tablet,/unread,CASUS,DFA,DFT,HZDR,PGI-1/IAS-1},
file = {/Users/wasmer/Nextcloud/Zotero/Moldabekov et al_2023_Non-empirical Mixing Coefficient for Hybrid XC Functionals from Analysis of the.pdf;/Users/wasmer/Zotero/storage/WGXJ5PMF/acs.jpclett.html}
}
@@ -6315,7 +7690,7 @@
urldate = {2023-01-02},
abstract = {Machine learning (ML) approaches enable large-scale atomistic simulations with near-quantum-mechanical accuracy. With the growing availability of these methods there arises a need for careful validation, particularly for physically agnostic models - that is, for potentials which extract the nature of atomic interactions from reference data. Here, we review the basic principles behind ML potentials and their validation for atomic-scale materials modeling. We discuss best practice in defining error metrics based on numerical performance as well as physically guided validation. We give specific recommendations that we hope will be useful for the wider community, including those researchers who intend to use ML potentials for materials "off the shelf".},
pubstate = {preprint},
- keywords = {_tablet,benchmarking,best practices,cross-validation,experimental data,GAP,how-to,ML,MLP,model evaluation,model validation,models comparison,MTP,numerical errors,SOAP,SOTA,tutorial},
+ keywords = {\_tablet,benchmarking,best practices,cross-validation,experimental data,GAP,how-to,ML,MLP,model evaluation,model validation,models comparison,MTP,numerical errors,SOAP,SOTA,tutorial},
file = {/Users/wasmer/Nextcloud/Zotero/Morrow et al_2022_How to validate machine-learned interatomic potentials.pdf;/Users/wasmer/Zotero/storage/TW3TCHB3/2211.html}
}
@@ -6328,7 +7703,7 @@
abstract = {Magnetic impurities and their long-range interaction (ferromagnetic) order play pivotal roles in the topological phase transition from QSHI to QAHI. This transition transforms helical edge states belonging to the QSHI for 2D TIs (surface states for 3D TI) to the chiral edge states in QAHI for 2D TIs (surface states for 3D TI). Due to such chiral states, the QAHIs forbid back-scattering in electron conducting channels, which in turn provide passionless current and increase energy efficiency for conduct- ing channels. The chiral states are consist of single spin electrons which provide spin currents from conventional charge currents. Regarding the properties of QAHIs, the QAHIs opens a new venue for low-energy elec- tronics, spintronics and quantum computation [9]. Independently, the V- [10] and Cr-doped [11] as well as their co-doping [12] (Sb, Bi)2 Te3 shows stable QAHE but with very low temperatures (≤ 0.5K). In this high throughput ab-initio work, we will investigate other possible co-doping, dimer calculations, from the d-block elements in 3D TI Bi2 Te3 . For this purpose, we have extended AiiDA-KKR plugins by developing combine- impurity workflow called combine imps wc using GF formulation of DFT code (KKR-GF method) and the new workflow is capable to run multi- impurity calculations. Here, the dimer calculations are in the main fo- cus, and from the calculation results we will analyze Heisenberg isotropic collinear interaction (Jij ), Dzyaloshinskii–Moriya interaction (DMI, Dij ), and their ratio for each possible impurity couple. Finally, using the ob- tained Jij data we have implemented some linear regression machine learn- ing tools to understand better the dependency of Jij on some well-known factors e.g. inter-impurity distance, electronegativity. Our results from the notion of this work will give a list of some potential impurities and after their potential impurity combinations for stable QAHE. It will also render an impression of implementation of machine learning approach for designing better magnetic interactions in TIs.},
langid = {english},
pagetotal = {85},
- keywords = {_tablet,AiiDA,aiida-kkr,Hall QAHE,impurity embedding,juKKR,KKR,master-thesis,PGI-1/IAS-1,thesis},
+ keywords = {\_tablet,AiiDA,aiida-kkr,Hall QAHE,impurity embedding,juKKR,KKR,master-thesis,PGI-1/IAS-1,thesis},
file = {/Users/wasmer/Nextcloud/Zotero/Mozumder_2022_Design of magnetic interactions in doped topological insulators.pdf}
}
@@ -6460,7 +7835,7 @@
url = {https://doi.org/10.1021/acs.chemrev.1c00021},
urldate = {2021-08-16},
abstract = {The first step in the construction of a regression model or a data-driven analysis, aiming to predict or elucidate the relationship between the atomic-scale structure of matter and its properties, involves transforming the Cartesian coordinates of the atoms into a suitable representation. The development of atomic-scale representations has played, and continues to play, a central role in the success of machine-learning methods for chemistry and materials science. This review summarizes the current understanding of the nature and characteristics of the most commonly used structural and chemical descriptions of atomistic structures, highlighting the deep underlying connections between different frameworks and the ideas that lead to computationally efficient and universally applicable models. It emphasizes the link between properties, structures, their physical chemistry, and their mathematical description, provides examples of recent applications to a diverse set of chemical and materials science problems, and outlines the open questions and the most promising research directions in the field.},
- keywords = {_tablet,ACSF,descriptor comparison,descriptor dimred,descriptors,MBTR,ML,review,SOAP},
+ keywords = {\_tablet,ACSF,descriptor comparison,descriptor dimred,descriptors,MBTR,ML,review,SOAP},
file = {/Users/wasmer/Nextcloud/Zotero/Musil et al_2021_Physics-Inspired Structural Representations for Molecules and Materials.pdf}
}
@@ -6474,10 +7849,27 @@
url = {http://arxiv.org/abs/2101.04673},
urldate = {2021-05-30},
abstract = {The first step in the construction of a regression model or a data-driven analysis framework for matter at the atomic scale involves transforming the Cartesian coordinates that describe the positions of the atoms in the form of a representation that obeys the same symmetries as the properties of interest, and in general reflects the physical nature of the problem. The link between properties, structures, their physical chemistry and their mathematical description is strongest when it comes to applications aimed at determining a precise correspondence between atomic configurations and the quantities that one might compute by a quantum mechanical electronic-structure calculation or measure experimentally. The development of atomic-scale representations have played, and continue to play, a central role in the success of machine-learning methods that rely on this correspondence, such as interatomic potentials, as well as generic property models, structural classifiers and low-dimensional maps of structures and datasets. This review summarizes the current understanding of the nature and characteristics of the most commonly used structural and chemical descriptions of molecules and materials, highlighting the deep underlying connections between different frameworks, and the ideas that lead to computationally efficient and universally applicable models. It gives examples of recent applications to a diverse set of chemical and materials science problems, and outlines the open questions and the most promising research directions in the field.},
- keywords = {_tablet,ACSF,descriptor dimred,descriptors,descriptors analysis,MBTR,ML,review,SOAP},
+ keywords = {\_tablet,ACSF,descriptor dimred,descriptors,descriptors analysis,MBTR,ML,review,SOAP},
file = {/Users/wasmer/Nextcloud/Zotero/Musil et al_2021_Physics-inspired structural representations for molecules and materials.pdf;/Users/wasmer/Zotero/storage/EXTUHGNH/2101.html}
}
+@article{nadj-pergeObservationMajoranaFermions2014,
+ title = {Observation of {{Majorana}} Fermions in Ferromagnetic Atomic Chains on a Superconductor},
+ author = {Nadj-Perge, Stevan and Drozdov, Ilya K. and Li, Jian and Chen, Hua and Jeon, Sangjun and Seo, Jungpil and MacDonald, Allan H. and Bernevig, B. Andrei and Yazdani, Ali},
+ date = {2014-10-31},
+ journaltitle = {Science},
+ volume = {346},
+ number = {6209},
+ pages = {602--607},
+ publisher = {{American Association for the Advancement of Science}},
+ doi = {10.1126/science.1259327},
+ url = {https://www.science.org/doi/10.1126/science.1259327},
+ urldate = {2023-05-10},
+ abstract = {Majorana fermions are predicted to localize at the edge of a topological superconductor, a state of matter that can form when a ferromagnetic system is placed in proximity to a conventional superconductor with strong spin-orbit interaction. With the goal of realizing a one-dimensional topological superconductor, we have fabricated ferromagnetic iron (Fe) atomic chains on the surface of superconducting lead (Pb). Using high-resolution spectroscopic imaging techniques, we show that the onset of superconductivity, which gaps the electronic density of states in the bulk of the Fe chains, is accompanied by the appearance of zero-energy end-states. This spatially resolved signature provides strong evidence, corroborated by other observations, for the formation of a topological phase and edge-bound Majorana fermions in our atomic chains.},
+ keywords = {/unread,Ferromagnetism,Majorana,MZM,physics,rec-by-da-silva,superconductor},
+ file = {/Users/wasmer/Nextcloud/Zotero/Nadj-Perge et al_2014_Observation of Majorana fermions in ferromagnetic atomic chains on a.pdf}
+}
+
@article{nagaosaTopologicalPropertiesDynamics2013,
title = {Topological Properties and Dynamics of Magnetic Skyrmions},
author = {Nagaosa, Naoto and Tokura, Yoshinori},
@@ -6499,6 +7891,26 @@
file = {/Users/wasmer/Nextcloud/Zotero/Nagaosa_Tokura_2013_Topological properties and dynamics of magnetic skyrmions.pdf}
}
+@article{nandiCheapTurnsSuperior2022,
+ title = {Cheap {{Turns Superior}}: {{A Linear Regression-Based Correction Method}} to {{Reaction Energy}} from the {{DFT}}},
+ shorttitle = {Cheap {{Turns Superior}}},
+ author = {Nandi, Surajit and Busk, Jonas and Jørgensen, Peter Bjørn and Vegge, Tejs and Bhowmik, Arghya},
+ date = {2022-10-10},
+ journaltitle = {Journal of Chemical Information and Modeling},
+ shortjournal = {J. Chem. Inf. Model.},
+ volume = {62},
+ number = {19},
+ pages = {4727--4735},
+ publisher = {{American Chemical Society}},
+ issn = {1549-9596},
+ doi = {10.1021/acs.jcim.2c00760},
+ url = {https://doi.org/10.1021/acs.jcim.2c00760},
+ urldate = {2023-06-28},
+ abstract = {Workflows to predict chemical reaction networks based on density functional theory (DFT) are prone to systematic errors in reaction energy due to the extensive use of cheap DFT exchange–correlation functionals to limit computational cost. Recently, machine learning-based models are increasingly applied to mitigate this problem. However, machine learning models require systems similar to trained data, and the models often perform poorly for out-of-distribution systems. Here, we present a simple bond-based correction method that improves the accuracy of DFT-derived reaction energies. It is based on linear regression, and the correction terms for each bond are derived from reactions among the QM9 data set. We demonstrate the effectiveness of this method with three DFT functionals in three different rungs of Jacob’s ladder. The simple correction method is effective for all rungs but especially so for the cheapest PBE functional. Finally, we applied the correction method to a few reactions with molecules significantly different from those in the QM9 data set that was used to fit the linear regression model. Once corrected by this method, we found that the DFT reaction energies for such out-of-distribution reactions are within 0.05 eV of the G4MP2 method.},
+ keywords = {/unread,AML,chemical reaction,chemical reaction network,chemistry,delta learning,DFA,DFT,GGA,LDA,ML-DFT,ML-ESM,molecules,PBE,prediction of reaction energy,QM9},
+ file = {/Users/wasmer/Nextcloud/Zotero/Nandi et al_2022_Cheap Turns Superior.pdf;/Users/wasmer/Zotero/storage/SRYNBRK7/acs.jcim.html}
+}
+
@article{narayanAssessingSinglecellTranscriptomic2021,
title = {Assessing Single-Cell Transcriptomic Variability through Density-Preserving Data Visualization},
author = {Narayan, Ashwin and Berger, Bonnie and Cho, Hyunghoon},
@@ -6550,7 +7962,7 @@
url = {http://arxiv.org/abs/2201.11647},
urldate = {2023-02-23},
abstract = {We investigate the potential of supervised machine learning to propagate a quantum system in time. While Markovian dynamics can be learned easily, given a sufficient amount of data, non-Markovian systems are non-trivial and their description requires the memory knowledge of past states. Here we analyse the feature of such memory by taking a simple 1D Heisenberg model as many-body Hamiltonian, and construct a non-Markovian description by representing the system over the single-particle reduced density matrix. The number of past states required for this representation to reproduce the time-dependent dynamics is found to grow exponentially with the number of spins and with the density of the system spectrum. Most importantly, we demonstrate that neural networks can work as time propagators at any time in the future and that they can be concatenated in time forming an autoregression. Such neural-network autoregression can be used to generate long-time and arbitrary dense time trajectories. Finally, we investigate the time resolution needed to represent the system memory. We find two regimes: for fine memory samplings the memory needed remains constant, while longer memories are required for coarse samplings, although the total number of time steps remains constant. The boundary between these two regimes is set by the period corresponding to the highest frequency in the system spectrum, demonstrating that neural network can overcome the limitation set by the Shannon-Nyquist sampling theorem.},
- keywords = {_tablet,/unread,Condensed Matter - Mesoscale and Nanoscale Physics,Condensed Matter - Strongly Correlated Electrons,Quantum Physics},
+ keywords = {\_tablet,/unread,Condensed Matter - Mesoscale and Nanoscale Physics,Condensed Matter - Strongly Correlated Electrons,Quantum Physics},
file = {/Users/wasmer/Nextcloud/Zotero/Nelson et al_2022_Data-Driven Time Propagation of Quantum Systems with Neural Networks.pdf;/Users/wasmer/Zotero/storage/N33SL7SM/2201.html}
}
@@ -6589,7 +8001,7 @@
url = {http://arxiv.org/abs/2103.05510},
urldate = {2023-02-23},
abstract = {We introduce a machine-learning density-functional-theory formalism for the spinless Hubbard model in one dimension at both zero and finite temperature. In the zero-temperature case this establishes a one-to-one relation between the site occupation and the total energy, which is then minimised at the ground-state occupation. In contrast, at finite temperature the same relation is defined between the Helmholtz free energy and the equilibrium site occupation. Most importantly, both functionals are semi-local, so that they are independent from the size of the system under investigation and can be constructed over exact data for small systems. These 'exact' functionals are numerically defined by neural networks. We also define additional neural networks for finite-temperature thermodynamical quantities, such as the entropy and heat capacity. These can be either a functional of the ground-state site occupation or of the finite-temperature equilibrium site occupation. In the first case their equilibrium value does not correspond to an extremal point of the functional, while it does in the second case. Our work gives us access to finite-temperature properties of many-body systems in the thermodynamic limit.},
- keywords = {_tablet,exact diagonaiization,finite-temperature DFT,Hubbard model,lattice DFT,ML,ML-DFA,ML-DFT,NN,thermodynamics},
+ keywords = {\_tablet,exact diagonaiization,finite-temperature DFT,Hubbard model,lattice DFT,ML,ML-DFA,ML-DFT,NN,thermodynamics},
file = {/Users/wasmer/Nextcloud/Zotero/Nelson et al_2021_Machine-learning semi-local density functional theory for many-body lattice.pdf;/Users/wasmer/Zotero/storage/FYR6QCEJ/2103.html}
}
@@ -6610,7 +8022,7 @@
url = {http://arxiv.org/abs/1906.08534},
urldate = {2023-02-23},
abstract = {The magnetic properties of a material are determined by a subtle balance between the various interactions at play, a fact that makes the design of new magnets a daunting task. High-throughput electronic structure theory may help to explore the vast chemical space available and offers a design tool to the experimental synthesis. This method efficiently predicts the elementary magnetic properties of a compound and its thermodynamical stability, but it is blind to information concerning the magnetic critical temperature. Here we introduce a range of machine-learning models to predict the Curie temperature, \$T\_\textbackslash mathrm\{C\}\$, of ferromagnets. The models are constructed by using experimental data for about 2,500 known magnets and consider the chemical composition of a compound as the only feature determining \$T\_\textbackslash mathrm\{C\}\$. Thus, we are able to establish a one-to-one relation between the chemical composition and the critical temperature. We show that the best model can predict \$T\_\textbackslash mathrm\{C\}\$'s with an accuracy of about 50K. Most importantly our model is able to extrapolate the predictions to regions of the chemical space, where only a little fraction of the data was considered for training. This is demonstrated by tracing the \$T\_\textbackslash mathrm\{C\}\$ of binary intermetallic alloys along their composition space and for the Al-Co-Fe ternary system.},
- keywords = {_tablet,/unread,Condensed Matter - Materials Science,Physics - Computational Physics},
+ keywords = {\_tablet,/unread,Condensed Matter - Materials Science,Physics - Computational Physics},
file = {/Users/wasmer/Nextcloud/Zotero/Nelson_Sanvito_2019_Predicting the Curie temperature of ferromagnets using machine learning.pdf;/Users/wasmer/Zotero/storage/J4ASXLIA/1906.html}
}
@@ -6645,6 +8057,42 @@
file = {/Users/wasmer/Nextcloud/Zotero/Neupert et al_2021_Introduction to Machine Learning for the Sciences.pdf;/Users/wasmer/Zotero/storage/GE7KJ34Q/2102.html}
}
+@article{nguyenFastProperOrthogonal2023,
+ title = {Fast Proper Orthogonal Descriptors for Many-Body Interatomic Potentials},
+ author = {Nguyen, Ngoc-Cuong},
+ date = {2023-04-12},
+ journaltitle = {Physical Review B},
+ shortjournal = {Phys. Rev. B},
+ volume = {107},
+ number = {14},
+ pages = {144103},
+ publisher = {{American Physical Society}},
+ doi = {10.1103/PhysRevB.107.144103},
+ url = {https://link.aps.org/doi/10.1103/PhysRevB.107.144103},
+ urldate = {2023-06-30},
+ abstract = {The development of differentiable invariant descriptors for accurate representations of atomic environments plays a central role in the success of interatomic potentials for chemistry and materials science. We introduce a method to generate fast proper orthogonal descriptors for the construction of many-body interatomic potentials, and we discuss its relation to existing empirical and machine-learning interatomic potentials. A traditional way of implementing the proper orthogonal descriptors has a computational complexity that scales exponentially with the body order in terms of the number of neighbors. We present an algorithm to compute the proper orthogonal descriptors with a computational complexity that scales linearly with the number of neighbors irrespective of the body order. We show that our method can enable a more efficient implementation for a number of existing potentials, and we provide a scalable systematic framework to construct new many-body potentials. The new potentials are demonstrated on a data set of density functional theory calculations for tantalum and compared with other interatomic potentials.},
+ keywords = {ACSF,AML,bispectrum,descriptor comparison,descriptors,internal coordinate descriptor,kernel methods,linear regression,ML,MLP,MTP,nonlinear regression,POD descriptor,SNAP,SOAP},
+ file = {/Users/wasmer/Nextcloud/Zotero/Nguyen_2023_Fast proper orthogonal descriptors for many-body interatomic potentials.pdf;/Users/wasmer/Zotero/storage/3337UHFR/PhysRevB.107.html}
+}
+
+@article{nguyenProperOrthogonalDescriptors2023,
+ title = {Proper Orthogonal Descriptors for Efficient and Accurate Interatomic Potentials},
+ author = {Nguyen, Ngoc Cuong and Rohskopf, Andrew},
+ date = {2023-05-01},
+ journaltitle = {Journal of Computational Physics},
+ shortjournal = {Journal of Computational Physics},
+ volume = {480},
+ pages = {112030},
+ issn = {0021-9991},
+ doi = {10.1016/j.jcp.2023.112030},
+ url = {https://www.sciencedirect.com/science/article/pii/S0021999123001250},
+ urldate = {2023-07-01},
+ abstract = {We present the proper orthogonal descriptors for efficient and accuracy representation of the potential energy surface. The potential energy surface is represented as a many-body expansion of parametrized potentials in which the potentials are functions of atom positions and parameters. The proper orthogonal decomposition is employed to decompose the parametrized potentials into a set of proper orthogonal descriptors (PODs). Because of the rapid convergence of the proper orthogonal decomposition, relevant snapshots can be sampled exhaustively to represent the atomic neighborhood environment accurately with a small number of descriptors. The proper orthogonal descriptors are used to develop interatomic potentials by using a linear expansion of the descriptors and determining the expansion coefficients from a weighted least-squares regression against a density functional theory (DFT) training set. We present a comprehensive evaluation of the POD potentials on previously published DFT data sets comprising Li, Mo, Cu, Ni, Si, Ge, and Ta elements. The data sets represent a diverse pool of metals, transition metals, and semiconductors. The accuracy of the POD potentials are comparable to that of state-of-the-art machine learning potentials such as the spectral neighbor analysis potential (SNAP) and the atomic cluster expansion (ACE).},
+ langid = {english},
+ keywords = {ACE,ACSF,AML,benchmarking,bispectrum,descriptor comparison,descriptors,Julia,kernel methods,library,materials,ML,MLP,original publication,POD descriptor,SNAP,SOAP,transition metals,with-code},
+ file = {/Users/wasmer/Nextcloud/Zotero/Nguyen_Rohskopf_2023_Proper orthogonal descriptors for efficient and accurate interatomic potentials.pdf;/Users/wasmer/Zotero/storage/PYJFVV5U/S0021999123001250.html}
+}
+
@online{nigamCompletenessAtomicStructure2023,
title = {Completeness of {{Atomic Structure Representations}}},
author = {Nigam, Jigyasa and Pozdnyakov, Sergey N. and Huguenin-Dumittan, Kevin K. and Ceriotti, Michele},
@@ -6690,7 +8138,7 @@
url = {http://arxiv.org/abs/2202.01566},
urldate = {2022-02-04},
abstract = {Data-driven schemes that associate molecular and crystal structures with their microscopic properties share the need for a concise, effective description of the arrangement of their atomic constituents. Many types of models rely on descriptions of atom-centered environments, that are associated with an atomic property or with an atomic contribution to an extensive macroscopic quantity. Frameworks in this class can be understood in terms of atom-centered density correlations (ACDC), that are used as a basis for a body-ordered, symmetry-adapted expansion of the targets. Several other schemes, that gather information on the relationship between neighboring atoms using graph-convolutional (or message-passing) ideas, cannot be directly mapped to correlations centered around a single atom. We generalize the ACDC framework to include multi-centered information, generating representations that provide a complete linear basis to regress symmetric functions of atomic coordinates, and form the basis to systematize our understanding of both atom-centered and graph-convolutional machine-learning schemes.},
- keywords = {_tablet,ACDC,ACE,descriptors,GCN,GNN,ML,MPNN,NequIP,NN,representation learning,SOAP,unified theory},
+ keywords = {\_tablet,ACDC,ACE,descriptors,GCN,GNN,ML,MPNN,NequIP,NN,representation learning,SOAP,unified theory},
file = {/home/johannes/Nextcloud/Zotero/false;/Users/wasmer/Nextcloud/Zotero/Nigam et al_2022_Unified theory of atom-centered representations and graph convolutional.pdf}
}
@@ -6709,7 +8157,7 @@
url = {https://link.aps.org/doi/10.1103/PhysRevB.106.235114},
urldate = {2023-04-04},
abstract = {An efficient and accurate generalization of the removed-sphere method (RSM) to solve the Poisson equation for total charge density in a solid with space-filling convex Voronoi polyhedra (VPs) and any symmetry is presented. The generalized RSM avoids the use of multipoles and VP shape functions for cellular integrals, which have associated ill-convergent large, double-internal L sums in spherical-harmonic expansions, so that fast convergence in single-L sums is reached. Our RSM adopts full Ewald formulation to work for all configurations or when symmetry breaking occurs, such as for atomic displacements or elastic constant calculations. The structure-dependent coefficients AL that define RSM can be calculated once for a fixed structure and speed up the whole self-consistent-field procedure. The accuracy and rapid convergence properties are confirmed using two analytic models, including the Coulomb potential and energy. We then implement the full-potential RSM using the Green's function Korringa-Kohn-Rostoker (KKR) method for real applications and compare the results with other first-principle methods and experimental data, showing that they are equally as accurate.},
- keywords = {/unread,DFT,KKR,Poisson equation},
+ keywords = {\_tablet,/unread,DFT,KKR,Poisson equation},
file = {/Users/wasmer/Nextcloud/Zotero/Ning et al_2022_Full-potential KKR within the removed-sphere method.pdf}
}
@@ -6752,7 +8200,7 @@
issue = {1},
langid = {english},
keywords = {ACE,AML,collinear,Ferromagnetism,FM,magnetism,MD,ML,MLP,mMTP,MTP,original publication,PAW,spin-dependent,spin-polarized,VASP},
- file = {/Users/wasmer/Nextcloud/Zotero/Novikov et al_2022_Magnetic Moment Tensor Potentials for collinear spin-polarized materials2.pdf}
+ file = {/Users/wasmer/Nextcloud/Zotero/Novikov et al_2022_Magnetic Moment Tensor Potentials for collinear spin-polarized materials.pdf}
}
@article{ohCompleteQuantumHall2013,
@@ -6767,7 +8215,7 @@
doi = {10.1126/science.1237215},
url = {https://www.science.org/doi/10.1126/science.1237215},
urldate = {2022-05-13},
- keywords = {_tablet,Hall effect,Hall QAHE,Hall QHE,Hall QSHE,perspective},
+ keywords = {\_tablet,Hall effect,Hall QAHE,Hall QHE,Hall QSHE,perspective},
file = {/Users/wasmer/Nextcloud/Zotero/Oh_2013_The Complete Quantum Hall Trio.pdf}
}
@@ -6804,7 +8252,7 @@
url = {https://aip.scitation.org/doi/10.1063/5.0016005},
urldate = {2021-05-13},
abstract = {Faithfully representing chemical environments is essential for describing materials and molecules with machine learning approaches. Here, we present a systematic classification of these representations and then investigate (i) the sensitivity to perturbations and (ii) the effective dimensionality of a variety of atomic environment representations and over a range of material datasets. Representations investigated include atom centered symmetry functions, Chebyshev Polynomial Symmetry Functions (CHSF), smooth overlap of atomic positions, many-body tensor representation, and atomic cluster expansion. In area (i), we show that none of the atomic environment representations are linearly stable under tangential perturbations and that for CHSF, there are instabilities for particular choices of perturbation, which we show can be removed with a slight redefinition of the representation. In area (ii), we find that most representations can be compressed significantly without loss of precision and, further, that selecting optimal subsets of a representation method improves the accuracy of regression models built for a given dataset.},
- keywords = {_tablet,ACE,ACSF,descriptors,MBTR,ML,MTP,SNAP,SOAP},
+ keywords = {\_tablet,ACE,ACSF,descriptors,MBTR,ML,MTP,SNAP,SOAP},
file = {/Users/wasmer/Nextcloud/Zotero/Onat et al_2020_Sensitivity and dimensionality of atomic environment representations used for.pdf;/Users/wasmer/Zotero/storage/RQ8UAKFX/5.html}
}
@@ -6857,6 +8305,25 @@
file = {/Users/wasmer/Nextcloud/Zotero/Ouyang et al_2018_SISSO.pdf;/Users/wasmer/Zotero/storage/FPEWTJ64/PhysRevMaterials.2.html}
}
+@article{ouyangSISSOCompressedsensingMethod2018a,
+ title = {{{SISSO}}: {{A}} Compressed-Sensing Method for Identifying the Best Low-Dimensional Descriptor in an Immensity of Offered Candidates},
+ shorttitle = {{{SISSO}}},
+ author = {Ouyang, Runhai and Curtarolo, Stefano and Ahmetcik, Emre and Scheffler, Matthias and Ghiringhelli, Luca M.},
+ date = {2018-08-07},
+ journaltitle = {Physical Review Materials},
+ shortjournal = {Phys. Rev. Mater.},
+ volume = {2},
+ number = {8},
+ pages = {083802},
+ publisher = {{American Physical Society}},
+ doi = {10.1103/PhysRevMaterials.2.083802},
+ url = {https://link.aps.org/doi/10.1103/PhysRevMaterials.2.083802},
+ urldate = {2023-05-06},
+ abstract = {The lack of reliable methods for identifying descriptors—the sets of parameters capturing the underlying mechanisms of a material's property—is one of the key factors hindering efficient materials development. Here, we propose a systematic approach for discovering descriptors for materials' properties, within the framework of compressed-sensing-based dimensionality reduction. The sure independence screening and sparsifying operator (SISSO) tackles immense and correlated features spaces, and converges to the optimal solution from a combination of features relevant to the materials' property of interest. In addition, SISSO gives stable results also with small training sets. The methodology is benchmarked with the quantitative prediction of the ground-state enthalpies of octet binary materials (using ab initio data) and applied to the showcase example of predicting the metal/insulator classification of binaries (with experimental data). Accurate, predictive models are found in both cases. For the metal-insulator classification model, the predictive capability is tested beyond the training data: It rediscovers the available pressure-induced insulator-to-metal transitions and it allows for the prediction of yet unknown transition candidates, ripe for experimental validation. As a step forward with respect to previous model-identification methods, SISSO can become an effective tool for automatic materials development.},
+ keywords = {/unread,AML,library,ML,original publication,SISSO,symbolic regression},
+ file = {/Users/wasmer/Nextcloud/Zotero/Ouyang et al_2018_SISSO2.pdf;/Users/wasmer/Zotero/storage/T9DSGU5D/PhysRevMaterials.2.html}
+}
+
@article{oviedoInterpretableExplainableMachine2022,
title = {Interpretable and {{Explainable Machine Learning}} for {{Materials Science}} and {{Chemistry}}},
author = {Oviedo, Felipe and Ferres, Juan Lavista and Buonassisi, Tonio and Butler, Keith T.},
@@ -6994,6 +8461,18 @@
file = {/Users/wasmer/Zotero/storage/ARZ5YYBV/Parsaeifard and Goedecker - 2022 - Manifolds of quasi-constant SOAP and ACSF fingerpr.pdf}
}
+@online{patelGoogleWeHave2023,
+ title = {Google "We Have No Moat, And Neither Does OpenAI"},
+ author = {Patel, Dylan},
+ date = {2023-05-04},
+ url = {https://www.semianalysis.com/p/google-we-have-no-moat-and-neither},
+ urldate = {2023-05-06},
+ abstract = {Leaked Internal Google Document Claims Open Source AI Will Outcompete Google and OpenAI},
+ langid = {ngerman},
+ keywords = {Bard,business,ChatGPT,document leak,foundation models,Google,LLM,Open source,OpenAI},
+ file = {/Users/wasmer/Zotero/storage/3R7VPZGJ/google-we-have-no-moat-and-neither.html}
+}
+
@article{pathrudkarMachineLearningBased2022,
title = {Machine Learning Based Prediction of the Electronic Structure of Quasi-One-Dimensional Materials under Strain},
author = {Pathrudkar, Shashank and Yu, Hsuan Ming and Ghosh, Susanta and Banerjee, Amartya S.},
@@ -7062,6 +8541,39 @@
file = {/Users/wasmer/Nextcloud/Zotero/Peixoto et al_2020_Non-local effect of impurity states on the exchange coupling mechanism in.pdf;/Users/wasmer/Zotero/storage/DDIQNTSB/s41535-020-00288-0.html}
}
+@online{penzStructureDensitypotentialMapping2023,
+ title = {The Structure of the Density-Potential Mapping. {{Part II}}: {{Including}} Magnetic Fields},
+ shorttitle = {The Structure of the Density-Potential Mapping. {{Part II}}},
+ author = {Penz, Markus and Tellgren, Erik I. and Csirik, Mihály A. and Ruggenthaler, Michael and Laestadius, Andre},
+ date = {2023-03-02},
+ eprint = {2303.01357},
+ eprinttype = {arxiv},
+ eprintclass = {physics, physics:quant-ph},
+ doi = {10.48550/arXiv.2303.01357},
+ url = {http://arxiv.org/abs/2303.01357},
+ urldate = {2023-06-30},
+ abstract = {The Hohenberg-Kohn theorem of density-functional theory (DFT) is broadly considered the conceptual basis for a full characterization of an electronic system in its ground state by just the one-body particle density. In this Part\textasciitilde II of a series of two articles, we aim at clarifying the status of this theorem within different extensions of DFT including magnetic fields. We will in particular discuss current-density-functional theory (CDFT) and review the different formulations known in the literature, including the conventional paramagnetic CDFT and some non-standard alternatives. For the former, it is known that the Hohenberg-Kohn theorem is no longer valid due to counterexamples. Nonetheless, paramagnetic CDFT has the mathematical framework closest to standard DFT and, just like in standard DFT, non-differentiability of the density functional can be mitigated through Moreau-Yosida regularization. Interesting insights can be drawn from both Maxwell-Schr\textbackslash "odinger DFT and quantum-electrodynamical DFT, which are also discussed here.},
+ pubstate = {preprint},
+ keywords = {/unread,DFT,DFT theory,HK map,HKT,magnetism,Physics - Chemical Physics,Quantum Physics,review,review-of-DFT},
+ file = {/Users/wasmer/Nextcloud/Zotero/Penz et al_2023_The structure of the density-potential mapping.pdf;/Users/wasmer/Zotero/storage/G52G7MTD/2303.html}
+}
+
+@article{penzStructureDensityPotentialMapping2023,
+ title = {The {{Structure}} of {{Density-Potential Mapping}}. {{Part I}}: {{Standard Density-Functional Theory}}},
+ shorttitle = {The {{Structure}} of {{Density-Potential Mapping}}. {{Part I}}},
+ author = {Penz, Markus and Tellgren, Erik I. and Csirik, Mihály A. and Ruggenthaler, Michael and Laestadius, Andre},
+ date = {2023-03-30},
+ journaltitle = {ACS Physical Chemistry Au},
+ shortjournal = {ACS Phys. Chem Au},
+ publisher = {{American Chemical Society}},
+ doi = {10.1021/acsphyschemau.2c00069},
+ url = {https://doi.org/10.1021/acsphyschemau.2c00069},
+ urldate = {2023-06-30},
+ abstract = {The Hohenberg–Kohn theorem of density-functional theory (DFT) is broadly considered the conceptual basis for a full characterization of an electronic system in its ground state by just the one-body particle density. Part I of this review aims at clarifying the status of the Hohenberg–Kohn theorem within DFT and Part II at different extensions of the theory that include magnetic fields. We collect evidence that the Hohenberg–Kohn theorem does not so much form the basis of DFT, but is rather the consequence of a more comprehensive mathematical framework. Such results are especially useful when it comes to the construction of generalized DFTs.},
+ keywords = {/unread,DFT,DFT theory,HK map,HKT,review,review-of-DFT},
+ file = {/Users/wasmer/Nextcloud/Zotero/Penz et al_2023_The Structure of Density-Potential Mapping.pdf;/Users/wasmer/Zotero/storage/ASJHHVMZ/acsphyschemau.html}
+}
+
@article{pereiraChallengesTopologicalInsulator2021,
title = {Challenges of {{Topological Insulator Research}}: {{Bi2Te3 Thin Films}} and {{Magnetic Heterostructures}}},
shorttitle = {Challenges of {{Topological Insulator Research}}},
@@ -7112,6 +8624,19 @@
file = {/Users/wasmer/Nextcloud/Zotero/Pfau_2020_iAb initio-i solution of the many-electron Schrödinger equation with deep.pdf;/Users/wasmer/Zotero/storage/7HFHVNYZ/PhysRevResearch.2.html}
}
+@online{pikulinProtocolIdentifyTopological2021,
+ title = {Protocol to Identify a Topological Superconducting Phase in a Three-Terminal Device},
+ author = {Pikulin, Dmitry I. and family=Heck, given=Bernard, prefix=van, useprefix=true and Karzig, Torsten and Martinez, Esteban A. and Nijholt, Bas and Laeven, Tom and Winkler, Georg W. and Watson, John D. and Heedt, Sebastian and Temurhan, Mine and Svidenko, Vicky and Lutchyn, Roman M. and Thomas, Mason and family=Lange, given=Gijs, prefix=de, useprefix=true and Casparis, Lucas and Nayak, Chetan},
+ date = {2021-03-22},
+ url = {https://arxiv.org/abs/2103.12217v1},
+ urldate = {2023-06-26},
+ abstract = {We develop a protocol to determine the presence and extent of a topological phase with Majorana zero modes in a hybrid superconductor-semiconductor device. The protocol is based on conductance measurements in a three-terminal device with two normal leads and one superconducting lead. A radio-frequency technique acts as a proxy for the measurement of local conductance, allowing a rapid, systematic scan of the large experimental phase space of the device. Majorana zero modes cause zero bias conductance peaks at each end of the wire, so we identify promising regions of the phase space by filtering for this condition. To validate the presence of a topological phase, a subsequent measurement of the non-local conductance in these regions is used to detect a topological transition via the closing and reopening of the bulk energy gap. We define data analysis routines that allow for an automated and unbiased execution of the protocol. Our protocol is designed to screen out false positives, especially trivial Andreev bound states that mimic Majorana zero modes in local conductance. We apply the protocol to several examples of simulated data illustrating the detection of topological phases and the screening of false positives.},
+ langid = {english},
+ organization = {{arXiv.org}},
+ keywords = {/unread,experimental science,Majorana,MZM,physics,topological insulator,transport properties},
+ file = {/Users/wasmer/Nextcloud/Zotero/Pikulin et al_2021_Protocol to identify a topological superconducting phase in a three-terminal.pdf}
+}
+
@article{pilaniaDataBasedMethodsMaterials2020,
title = {Data-{{Based Methods}} for {{Materials Design}} and {{Discovery}}: {{Basic Ideas}} and {{General Methods}}},
shorttitle = {Data-{{Based Methods}} for {{Materials Design}} and {{Discovery}}},
@@ -7150,6 +8675,23 @@
file = {/Users/wasmer/Nextcloud/Zotero/Pinheiro et al_2021_Choosing the right molecular machine learning potential.pdf}
}
+@online{podryabinkinMLIP3ActiveLearning2023,
+ title = {{{MLIP-3}}: {{Active}} Learning on Atomic Environments with {{Moment Tensor Potentials}}},
+ shorttitle = {{{MLIP-3}}},
+ author = {Podryabinkin, Evgeny and Garifullin, Kamil and Shapeev, Alexander and Novikov, Ivan},
+ date = {2023-04-25},
+ eprint = {2304.13144},
+ eprinttype = {arxiv},
+ eprintclass = {physics},
+ doi = {10.48550/arXiv.2304.13144},
+ url = {http://arxiv.org/abs/2304.13144},
+ urldate = {2023-05-26},
+ abstract = {Nowadays, academic research relies not only on sharing with the academic community the scientific results obtained by research groups while studying certain phenomena, but also on sharing computer codes developed within the community. In the field of atomistic modeling these were software packages for classical atomistic modeling, later -- quantum-mechanical modeling, and now with the fast growth of the field of machine-learning potentials, the packages implementing such potentials. In this paper we present the MLIP-3 package for constructing moment tensor potentials and performing their active training. This package builds on the MLIP-2 package (Novikov et al. (2020), The MLIP package: moment tensor potentials with MPI and active learning. Machine Learning: Science and Technology, 2(2), 025002.), however with a number of improvements, including active learning on atomic neighborhoods of a possibly large atomistic simulation.},
+ pubstate = {preprint},
+ keywords = {active learning,active learning online,AML,library,ML,MLP,MTP,with-code},
+ file = {/Users/wasmer/Nextcloud/Zotero/Podryabinkin et al_2023_MLIP-3.pdf;/Users/wasmer/Zotero/storage/QTPXSGSW/2304.html}
+}
+
@article{poelkingBenchMLExtensiblePipelining2022,
title = {{{BenchML}}: An Extensible Pipelining Framework for Benchmarking Representations of Materials and Molecules at Scale},
shorttitle = {{{BenchML}}},
@@ -7167,10 +8709,28 @@
urldate = {2023-03-19},
abstract = {We introduce a machine-learning (ML) framework for high-throughput benchmarking of diverse representations of chemical systems against datasets of materials and molecules. The guiding principle underlying the benchmarking approach is to evaluate raw descriptor performance by limiting model complexity to simple regression schemes while enforcing best ML practices, allowing for unbiased hyperparameter optimization, and assessing learning progress through learning curves along series of synchronized train-test splits. The resulting models are intended as baselines that can inform future method development, in addition to indicating how easily a given dataset can be learnt. Through a comparative analysis of the training outcome across a diverse set of physicochemical, topological and geometric representations, we glean insight into the relative merits of these representations as well as their interrelatedness.},
langid = {english},
- keywords = {ACSF,AML,benchmarking,CM,Coulomb matrix,descriptor comparison,descriptors,DScribe,ECFP descriptor,GYLM descriptor,KRR,library,linear regression,materials,MBTR,ML,MLOps,molecules,QM7b,QM9,SISSO,SOAP,with-code,workflows},
+ keywords = {\_tablet,ACSF,AML,benchmarking,CM,Coulomb matrix,descriptor comparison,descriptors,DScribe,ECFP descriptor,GYLM descriptor,KRR,library,linear regression,materials,MBTR,ML,MLOps,molecules,QM7b,QM9,SISSO,SOAP,with-code,workflows},
file = {/Users/wasmer/Zotero/storage/QXAEL2PM/Poelking et al_2022_BenchML.pdf}
}
+@article{pokharelExactConstraintsAppropriate2022,
+ title = {Exact Constraints and Appropriate Norms in Machine-Learned Exchange-Correlation Functionals},
+ author = {Pokharel, Kanun and Furness, James W. and Yao, Yi and Blum, Volker and Irons, Tom J. P. and Teale, Andrew M. and Sun, Jianwei},
+ date = {2022-11-03},
+ journaltitle = {The Journal of Chemical Physics},
+ shortjournal = {The Journal of Chemical Physics},
+ volume = {157},
+ number = {17},
+ pages = {174106},
+ issn = {0021-9606},
+ doi = {10.1063/5.0111183},
+ url = {https://doi.org/10.1063/5.0111183},
+ urldate = {2023-06-30},
+ abstract = {Machine learning techniques have received growing attention as an alternative strategy for developing general-purpose density functional approximations, augmenting the historically successful approach of human-designed functionals derived to obey mathematical constraints known for the exact exchange-correlation functional. More recently, efforts have been made to reconcile the two techniques, integrating machine learning and exact-constraint satisfaction. We continue this integrated approach, designing a deep neural network that exploits the exact constraint and appropriate norm~philosophy to de-orbitalize the strongly constrained and appropriately normed (SCAN) functional. The deep neural network is trained to replicate the SCAN functional from only electron density and local derivative information, avoiding the use of the orbital-dependent kinetic energy density. The performance and transferability of the machine-learned functional are demonstrated for molecular and periodic systems.},
+ keywords = {DFT,exact constraints,LC20,materials,ML,ML-DFA,ML-DFT,ML-ESM,molecules,prediction of lattice constant,SCAN,spin-dependent,spin-scaling,TensorFlow},
+ file = {/Users/wasmer/Nextcloud/Zotero/Pokharel et al_2022_Exact constraints and appropriate norms in machine-learned exchange-correlation.pdf;/Users/wasmer/Zotero/storage/V9887FVI/Exact-constraints-and-appropriate-norms-in-machine.html}
+}
+
@online{polakExtractingAccurateMaterials2023,
title = {Extracting {{Accurate Materials Data}} from {{Research Papers}} with {{Conversational Language Models}} and {{Prompt Engineering}} -- {{Example}} of {{ChatGPT}}},
author = {Polak, Maciej P. and Morgan, Dane},
@@ -7244,7 +8804,7 @@
url = {https://link.aps.org/doi/10.1103/PhysRevLett.125.166001},
urldate = {2021-05-13},
abstract = {Many-body descriptors are widely used to represent atomic environments in the construction of machine-learned interatomic potentials and more broadly for fitting, classification, and embedding tasks on atomic structures. There is a widespread belief in the community that three-body correlations are likely to provide an overcomplete description of the environment of an atom. We produce several counterexamples to this belief, with the consequence that any classifier, regression, or embedding model for atom-centered properties that uses three- (or four)-body features will incorrectly give identical results for different configurations. Writing global properties (such as total energies) as a sum of many atom-centered contributions mitigates the impact of this fundamental deficiency—explaining the success of current “machine-learning” force fields. We anticipate the issues that will arise as the desired accuracy increases, and suggest potential solutions.},
- keywords = {_tablet,3-body order descriptors,descriptors,descriptors analysis,GPR,incompleteness,MBTR,ML,SOAP},
+ keywords = {\_tablet,3-body order descriptors,descriptors,descriptors analysis,GPR,incompleteness,MBTR,ML,SOAP},
file = {/Users/wasmer/Nextcloud/Zotero/Pozdnyakov et al_2020_Incompleteness of Atomic Structure Representations.pdf;/Users/wasmer/Zotero/storage/5QHMC4CR/PhysRevLett.125.html}
}
@@ -7261,6 +8821,22 @@
file = {/Users/wasmer/Nextcloud/Zotero/Pozdnyakov_Ceriotti_2022_Incompleteness of graph convolutional neural networks for points clouds in.pdf;/Users/wasmer/Zotero/storage/ZKHDUH3X/2201.html}
}
+@online{pozdnyakovSmoothExactRotational2023,
+ title = {Smooth, Exact Rotational Symmetrization for Deep Learning on Point Clouds},
+ author = {Pozdnyakov, Sergey N. and Ceriotti, Michele},
+ date = {2023-05-30},
+ eprint = {2305.19302},
+ eprinttype = {arxiv},
+ eprintclass = {cond-mat, physics:physics},
+ doi = {10.48550/arXiv.2305.19302},
+ url = {http://arxiv.org/abs/2305.19302},
+ urldate = {2023-06-01},
+ abstract = {Point clouds are versatile representations of 3D objects and have found widespread application in science and engineering. Many successful deep-learning models have been proposed that use them as input. Some application domains require incorporating exactly physical constraints, including chemical and materials modeling which we focus on in this paper. These constraints include smoothness, and symmetry with respect to translations, rotations, and permutations of identical particles. Most existing architectures in other domains do not fulfill simultaneously all of these requirements and thus are not applicable to atomic-scale simulations. Many of them, however, can be straightforwardly made to incorporate all the physical constraints except for rotational symmetry. We propose a general symmetrization protocol that adds rotational equivariance to any given model while preserving all the other constraints. As a demonstration of the potential of this idea, we introduce the Point Edge Transformer (PET) architecture, which is not intrinsically equivariant but achieves state-of-the-art performance on several benchmark datasets of molecules and solids. A-posteriori application of our general protocol makes PET exactly equivariant, with minimal changes to its accuracy. By alleviating the need to explicitly incorporate rotational symmetry within the model, our method bridges the gap between the approaches used in different communities, and simplifies the design of deep-learning schemes for chemical and materials modeling.},
+ pubstate = {preprint},
+ keywords = {\_tablet,AML,equivariant,equivariant alternative,GNN,ML,MPNN,point cloud data,rotational symmetry,simplification,SO(3),symmetrization,transformer,with-code},
+ file = {/Users/wasmer/Nextcloud/Zotero/Pozdnyakov_Ceriotti_2023_Smooth, exact rotational symmetrization for deep learning on point clouds.pdf;/Users/wasmer/Zotero/storage/W32HXDSQ/2305.html}
+}
+
@online{probstGrowingPainsReacting2022,
title = {Growing Pains: {{Reacting}} to Negative Impacts of Deep Learning on Machine Learning for Chemistry},
shorttitle = {Growing Pains},
@@ -7273,7 +8849,7 @@
abstract = {While the introduction of practical deep learning has driven progress across scientific fields, recent research highlighted that deep learning has potential negative impacts on the scientific community and society as a whole. An ever-growing need for more computational resources may exacerbate the concentration of funding and the exclusiveness of research between countries, sectors, and institutions. Here, I introduce recent concerns and considerations of the machine learning research community and present potential solutions.},
langid = {english},
pubstate = {preprint},
- keywords = {AML,best practices,chemistry,computational cost,cost analysis,criticism,Deep learning,LLM,ML,ML cost analysis,ML ethics,model evaluation,skepticism,small data},
+ keywords = {AML,best practices,chemistry,computational cost,cost analysis,criticism,Deep learning,LLM,ML,ML cost analysis,ML ethics,model evaluation,model reporting,skepticism,small data},
file = {/Users/wasmer/Nextcloud/Zotero/Probst_2022_Growing pains.pdf}
}
@@ -7314,27 +8890,11 @@
url = {https://link.springer.com/book/10.1007/978-3-540-85416-6},
urldate = {2022-06-18},
langid = {english},
- keywords = {_tablet,condensed matter,graduate,magnetism,textbook},
+ keywords = {\_tablet,condensed matter,graduate,magnetism,textbook},
file = {/Users/wasmer/Nextcloud/Zotero/Quantum Theory of Magnetism.pdf;/Users/wasmer/Zotero/storage/ULV44ULF/978-3-540-85416-6.html}
}
-@unpublished{rackersCrackingQuantumScaling2022,
- title = {Cracking the {{Quantum Scaling Limit}} with {{Machine Learned Electron Densities}}},
- author = {Rackers, Joshua A. and Tecot, Lucas and Geiger, Mario and Smidt, Tess E.},
- date = {2022-02-10},
- eprint = {2201.03726},
- eprinttype = {arxiv},
- eprintclass = {cond-mat, physics:physics},
- publisher = {{arXiv}},
- doi = {10.48550/arXiv.2201.03726},
- url = {http://arxiv.org/abs/2201.03726},
- urldate = {2022-05-18},
- abstract = {A long-standing goal of science is to accurately solve the Schr\textbackslash "odinger equation for large molecular systems. The poor scaling of current quantum chemistry algorithms on classical computers imposes an effective limit of about a few dozen atoms for which we can calculate molecular electronic structure. We present a machine learning (ML) method to break through this scaling limit and make quantum chemistry calculations of very large systems possible. We show that Euclidean Neural Networks can be trained to predict the electron density with high fidelity from limited data. Learning the electron density allows us to train a machine learning model on small systems and make accurate predictions on large ones. We show that this ML electron density model can break through the quantum scaling limit and calculate the electron density of systems of thousands of atoms with quantum accuracy.},
- keywords = {_tablet,charge density,e3nn,ENN,ML,ML-DFT,ML-ESM,molecules,prediction of electron density,script,target: density,transfer learning,with-code},
- file = {/Users/wasmer/Nextcloud/Zotero/Rackers et al_2022_Cracking the Quantum Scaling Limit with Machine Learned Electron Densities.pdf;/Users/wasmer/Zotero/storage/X9XGJLLI/2201.html}
-}
-
-@online{rackersCrackingQuantumScaling2022a,
+@online{rackersCrackingQuantumScaling2022,
title = {Cracking the {{Quantum Scaling Limit}} with {{Machine Learned Electron Densities}}},
author = {Rackers, Joshua A. and Tecot, Lucas and Geiger, Mario and Smidt, Tess E.},
date = {2022-02-10},
@@ -7486,10 +9046,42 @@
urldate = {2022-09-27},
abstract = {Machine learning plays an increasingly important role in many areas of chemistry and materials science, e.g. to predict materials properties, to accelerate simulations, to design new materials, and to predict synthesis routes of new materials. Graph neural networks (GNNs) are one of the fastest growing classes of machine learning models. They are of particular relevance for chemistry and materials science, as they directly work on a graph or structural representation of molecules and materials and therefore have full access to all relevant information required to characterize materials. In this review article, we provide an overview of the basic principles of GNNs, widely used datasets, and state-of-the-art architectures, followed by a discussion of a wide range of recent applications of GNNs in chemistry and materials science, and concluding with a road-map for the further development and application of GNNs.},
pubstate = {preprint},
- keywords = {_tablet,GCN,GNN,molecules,review,solids},
+ keywords = {\_tablet,GCN,GNN,molecules,review,solids},
file = {/Users/wasmer/Nextcloud/Zotero/Reiser et al_2022_Graph neural networks for materials science and chemistry.pdf;/Users/wasmer/Zotero/storage/IVEGXDHZ/2208.html}
}
+@article{renLigandOptimizationExchange2022,
+ title = {Ligand {{Optimization}} of {{Exchange Interaction}} in {{Co}}({{II}}) {{Dimer Single Molecule Magnet}} by {{Machine Learning}}},
+ author = {Ren, Sijin and Fonseca, Eric and Perry, William and Cheng, Hai-Ping and Zhang, Xiao-Guang and Hennig, Richard G.},
+ date = {2022-02-03},
+ journaltitle = {The Journal of Physical Chemistry A},
+ shortjournal = {J. Phys. Chem. A},
+ volume = {126},
+ number = {4},
+ pages = {529--535},
+ publisher = {{American Chemical Society}},
+ issn = {1089-5639},
+ doi = {10.1021/acs.jpca.1c08950},
+ url = {https://doi.org/10.1021/acs.jpca.1c08950},
+ urldate = {2023-05-06},
+ abstract = {Designing single-molecule magnets (SMMs) for potential applications in quantum computing and high-density data storage requires tuning their magnetic properties, especially the strength of the magnetic interaction. These properties can be characterized by first-principles calculations based on density functional theory (DFT). In this work, we study the experimentally synthesized Co(II) dimer (Co2(C5NH5)4(μ-PO2(CH2C6H5)2)3) SMM with the goal to control the exchange energy, ΔEJ, between the Co atoms through tuning of the capping ligands. The experimentally synthesized Co(II) dimer molecule has a very small ΔEJ {$<$} 1 meV. We assemble a DFT data set of 1081 ligand substitutions for the Co(II) dimer. The ligand exchange provides a broad range of exchange energies, ΔEJ, from +50 to −200 meV, with 80\% of the ligands yielding a small ΔEJ {$<$} 10 meV. We identify descriptors for the classification and regression of ΔEJ using gradient boosting machine learning models. We compare one-hot encoded, structure-based, and chemical descriptors consisting of the HOMO/LUMO energies of the individual ligands and the maximum electronegativity difference and bond order for the ligand atom connecting to Co. We observe a similar overall performance with the chemical descriptors outperforming the other descriptors. We show that the exchange coupling, ΔEJ, is correlated to the difference in the average bridging angle between the ferromagnetic and antiferromagnetic states, similar to the Goodenough–Kanamori rules.},
+ keywords = {/unread,AML,compositional descriptors,descriptor comparison,descriptors,DFT,magnetism,ML,molecular magnet,prediction of Exc,SB descriptors},
+ file = {/Users/wasmer/Nextcloud/Zotero/Ren et al_2022_Ligand Optimization of Exchange Interaction in Co(II) Dimer Single Molecule.pdf;/Users/wasmer/Zotero/storage/NZ36VI4U/acs.jpca.html}
+}
+
+@online{rinaldiNoncollinearMagneticAtomic2023,
+ title = {Non-Collinear {{Magnetic Atomic Cluster Expansion}} for {{Iron}}},
+ author = {Rinaldi, Matteo and Mrovec, Matous and Bochkarev, Anton and Lysogorskiy, Yury and Drautz, Ralf},
+ date = {2023-05-24},
+ url = {https://arxiv.org/abs/2305.15137v1},
+ urldate = {2023-07-01},
+ abstract = {The Atomic Cluster Expansion (ACE) provides a formally complete basis for the local atomic environment. ACE is not limited to representing energies as a function of atomic positions and chemical species, but can be generalized to vectorial or tensorial properties and to incorporate further degrees of freedom (DOF). This is crucial for magnetic materials with potential energy surfaces that depend on atomic positions and atomic magnetic moments simultaneously. In this work, we employ the ACE formalism to develop a non-collinear magnetic ACE parametrization for the prototypical magnetic element Fe. The model is trained on a broad range of collinear and non-collinear magnetic structures calculated using spin density functional theory. We demonstrate that the non-collinear magnetic ACE is able to reproduce not only ground state properties of various magnetic phases of Fe but also the magnetic and lattice excitations that are essential for a correct description of the finite temperature behavior and properties of crystal defects.},
+ langid = {english},
+ organization = {{arXiv.org}},
+ keywords = {/unread},
+ file = {/Users/wasmer/Nextcloud/Zotero/Rinaldi et al_2023_Non-collinear Magnetic Atomic Cluster Expansion for Iron.pdf}
+}
+
@article{RiseQuantumMaterials2016,
title = {The Rise of Quantum Materials},
date = {2016-02},
@@ -7511,6 +9103,44 @@
file = {/Users/wasmer/Nextcloud/Zotero/2016_The rise of quantum materials.pdf;/Users/wasmer/Zotero/storage/YG3UAYEY/nphys3668.html}
}
+@online{robertsTensorNetworkLibraryPhysics2019,
+ title = {{{TensorNetwork}}: {{A Library}} for {{Physics}} and {{Machine Learning}}},
+ shorttitle = {{{TensorNetwork}}},
+ author = {Roberts, Chase and Milsted, Ashley and Ganahl, Martin and Zalcman, Adam and Fontaine, Bruce and Zou, Yijian and Hidary, Jack and Vidal, Guifre and Leichenauer, Stefan},
+ date = {2019-05-03},
+ eprint = {1905.01330},
+ eprinttype = {arxiv},
+ eprintclass = {cond-mat, physics:hep-th, physics:physics, stat},
+ doi = {10.48550/arXiv.1905.01330},
+ url = {http://arxiv.org/abs/1905.01330},
+ urldate = {2023-06-30},
+ abstract = {TensorNetwork is an open source library for implementing tensor network algorithms. Tensor networks are sparse data structures originally designed for simulating quantum many-body physics, but are currently also applied in a number of other research areas, including machine learning. We demonstrate the use of the API with applications both physics and machine learning, with details appearing in companion papers.},
+ pubstate = {preprint},
+ keywords = {AML,General ML,Google,image classification,library,ML,particle physics,Physics ML,prediction of ground-state properties,quantum computing,quantum information,spin,spin chain,spin-dependent,strongly correlated maeterials,tensor network},
+ file = {/Users/wasmer/Nextcloud/Zotero/Roberts et al_2019_TensorNetwork.pdf;/Users/wasmer/Zotero/storage/PF5AB8AA/1905.html}
+}
+
+@article{rochaMolecularSpintronics2005,
+ title = {Towards Molecular Spintronics},
+ author = {Rocha, Alexandre R. and García-suárez, Víctor M. and Bailey, Steve W. and Lambert, Colin J. and Ferrer, Jaime and Sanvito, Stefano},
+ date = {2005-04},
+ journaltitle = {Nature Materials},
+ shortjournal = {Nature Mater},
+ volume = {4},
+ number = {4},
+ pages = {335--339},
+ publisher = {{Nature Publishing Group}},
+ issn = {1476-4660},
+ doi = {10.1038/nmat1349},
+ url = {https://www.nature.com/articles/nmat1349},
+ urldate = {2023-06-30},
+ abstract = {The ability to manipulate electron spin in organic molecular materials offers a new and extremely tantalizing route towards spin electronics, both from fundamental and technological points of view. This is mainly due to the unquestionable advantage of weak spin–orbit and hyperfine interactions in organic molecules, which leads to the possibility of preserving spin-coherence over times and distances much longer than in conventional metals or semiconductors. Here we demonstrate theoretically that organic spin valves, obtained by sandwiching an organic molecule between magnetic contacts, can show a large bias-dependent magnetoresistance and that this can be engineered by an appropriate choice of molecules and anchoring groups. Our results, obtained through a combination of state-of-the-art non-equilibrium transport methods and density functional theory, show that although the magnitude of the effect varies with the details of the molecule, large magnetoresistance can be found both in the tunnelling and the metallic limit.},
+ issue = {4},
+ langid = {english},
+ keywords = {DFT,magnetism,molecular spintronics,molecules,organic chemistry,physics,review,Spintronics},
+ file = {/Users/wasmer/Nextcloud/Zotero/Rocha et al_2005_Towards molecular spintronics.pdf}
+}
+
@article{rodriguezComparativeStudyDifferent2022,
title = {A Comparative Study of Different Machine Learning Methods for Dissipative Quantum Dynamics},
author = {Rodríguez, Luis E. Herrera and Ullah, Arif and Espinosa, Kennet J. Rueda and Dral, Pavlo O. and Kananenka, Alexei A.},
@@ -7582,10 +9212,25 @@
urldate = {2022-05-13},
abstract = {Models that combine quantum mechanics (QM) with machine learning (ML) promise to deliver the accuracy of QM at the speed of ML. This hands-on tutorial introduces the reader to QM/ML models based on kernel learning, an elegant, systematically nonlinear form of ML. Pseudocode and a reference implementation are provided, enabling the reader to reproduce results from recent publications where atomization energies of small organic molecules are predicted using kernel ridge regression. © 2015 Wiley Periodicals, Inc.},
langid = {english},
- keywords = {_tablet,Coulomb matrix,GPR,kernel methods,KRR,ML,models,tutorial},
+ keywords = {\_tablet,Coulomb matrix,GPR,kernel methods,KRR,ML,models,tutorial},
file = {/Users/wasmer/Nextcloud/Zotero/Rupp_2015_Machine learning for quantum mechanics in a nutshell.pdf;/Users/wasmer/Zotero/storage/7CP5YBAD/qua.html}
}
+@article{rusinovTheoryGaplessSuperconductivity1969,
+ title = {On the {{Theory}} of {{Gapless Superconductivity}} in {{Alloys Containing Paramagnetic Impurities}}},
+ author = {Rusinov, A. I.},
+ date = {1969-01-01},
+ journaltitle = {Soviet Journal of Experimental and Theoretical Physics},
+ volume = {29},
+ pages = {1101},
+ issn = {1063-7761},
+ url = {https://ui.adsabs.harvard.edu/abs/1969JETP...29.1101R},
+ urldate = {2023-05-10},
+ keywords = {/unread,impurity embedding,magnetic impurity,magnetism,original publication,paramagnetic impurity,physics,rec-by-da-silva,superconductor,Yu-Shiba-Rusinov,Yu-Shiba-Rusinov state},
+ annotation = {ADS Bibcode: 1969JETP...29.1101R},
+ file = {/Users/wasmer/Nextcloud/Zotero/Rusinov_1969_On the Theory of Gapless Superconductivity in Alloys Containing Paramagnetic.pdf}
+}
+
@article{russmannAiiDAKKRPluginIts2021,
title = {The {{AiiDA-KKR}} Plugin and Its Application to High-Throughput Impurity Embedding into a Topological Insulator},
author = {Rüßmann, Philipp and Bertoldo, Fabian and Blügel, Stefan},
@@ -7602,7 +9247,7 @@
abstract = {The ever increasing availability of supercomputing resources led computer-based materials science into a new era of high-throughput calculations. Recently, Pizzi et al. introduced the AiiDA framework that provides a way to automate calculations while allowing to store the full provenance of complex workflows in a database. We present the development of the AiiDA-KKR plugin that allows to perform a large number of ab initio impurity embedding calculations based on the relativistic full-potential Korringa-Kohn-Rostoker Green function method. The capabilities of the AiiDA-KKR plugin are demonstrated with the calculation of several thousand impurities embedded into the prototypical topological insulator Sb2Te3. The results are collected in the JuDiT database which we use to investigate chemical trends as well as Fermi level and layer dependence of physical properties of impurities. This includes the study of spin moments, the impurity’s tendency to form in-gap states or its effect on the charge doping of the host-crystal. These properties depend on the detailed electronic structure of the impurity embedded into the host crystal which highlights the need for ab initio calculations in order to get accurate predictions.},
issue = {1},
langid = {english},
- keywords = {_tablet,AiiDA,aiida-kkr,defects,FZJ,impurity embedding,juKKR,KKR,PGI-1/IAS-1,physics,topological insulator},
+ keywords = {\_tablet,AiiDA,aiida-kkr,defects,FZJ,impurity embedding,juKKR,KKR,PGI-1/IAS-1,physics,topological insulator},
file = {/Users/wasmer/Nextcloud/Zotero/Rüßmann et al_2021_The AiiDA-KKR plugin and its application to high-throughput impurity embedding.pdf;/Users/wasmer/Zotero/storage/X4T36V7Q/s41524-020-00482-5.html}
}
@@ -7617,7 +9262,7 @@
url = {https://www.frontiersin.org/articles/10.3389/fmats.2022.825043},
urldate = {2022-08-11},
abstract = {Landau-Lifshitz-Gilbert (LLG) spin-dynamics calculations based on the extended Heisenberg Hamiltonian is an important tool in computational materials science involving magnetic materials. LLG simulations allow to bridge the gap from expensive quantum mechanical calculations with small unit cells to large supercells where the collective behavior of millions of spins can be studied. In this work we present the AiiDA-Spirit plugin that connects the spin-dynamics code Spirit to the AiiDA framework. AiiDA provides a Python interface that facilitates performing high-throughput calculations while automatically augmenting the calculations with metadata describing the data provenance between calculations in a directed acyclic graph. The AiiDA-Spirit interface thus provides an easy way for high-throughput spin-dynamics calculations. The interface to the AiiDA infrastructure furthermore has the advantage that input parameters for the extended Heisenberg model can be extracted from high-throughput first-principles calculations including a proper treatment of the data provenance that ensures reproducibility of the calculation results in accordance to the FAIR principles. We describe the layout of the AiiDA-Spirit plugin and demonstrate its capabilities using selected examples for LLG spin-dynamics and Monte Carlo calculations. Furthermore, the integration with first-principles calculations through AiiDA is demonstrated at the example of γ–Fe, where the complex spin-spiral ground state is investigated.},
- keywords = {_tablet,AiiDA,aiida-kkr,Heisenberg model,Jij,KKR,library,PGI-1/IAS-1,rec-by-ruess,spin dynamics,Spirit,with-code},
+ keywords = {\_tablet,AiiDA,aiida-kkr,Heisenberg model,Jij,KKR,library,PGI-1/IAS-1,rec-by-ruess,spin dynamics,Spirit,with-code},
file = {/Users/wasmer/Nextcloud/Zotero/Rüßmann et al_2022_The AiiDA-Spirit Plugin for Automated Spin-Dynamics Simulations and Multi-Scale.pdf}
}
@@ -7635,7 +9280,7 @@
urldate = {2022-05-30},
abstract = {The quasiparticle interference (QPI) technique is a powerful tool that allows to uncover the structure and properties of electronic structure of a material combined with scattering properties of defects at surfaces. Recently, this technique has been pivotal in proving the unique properties of the surface state of topological insulators which manifests itself in the absence of backscattering. Herein, a Green function-based formalism is derived for the ab initio computation of Fourier-transformed QPI images. The efficiency of the new implementation is shown at the examples of QPI that forms around magnetic and nonmagnetic defects at the Bi2Te3 surface. This method allows a deepened understanding of the scattering properties of topologically protected electrons off defects and is a useful tool in the study of quantum materials in the future.},
langid = {english},
- keywords = {_tablet,density functional theory,impurity scattering,Korringa–Kohn–Rostoker,quasiparticle interferences,topological insulators},
+ keywords = {\_tablet,density functional theory,impurity scattering,Korringa–Kohn–Rostoker,quasiparticle interferences,topological insulators},
file = {/Users/wasmer/Nextcloud/Zotero/Rüßmann et al_2021_Ab Initio Theory of Fourier-Transformed Quasiparticle Interference Maps and.pdf}
}
@@ -7705,6 +9350,22 @@
file = {/Users/wasmer/Zotero/storage/YLQYEHWE/Sadeghi et al. - 2013 - Metrics for measuring distances in configuration s.pdf}
}
+@online{salzbrennerDevelopmentsFurtherApplications2023,
+ title = {Developments and {{Further Applications}} of {{Ephemeral Data Derived Potentials}}},
+ author = {Salzbrenner, Pascal T. and Joo, Se Hun and Conway, Lewis J. and Cooke, Peter I. C. and Zhu, Bonan and Matraszek, Milosz P. and Witt, William C. and Pickard, Chris J.},
+ date = {2023-06-10},
+ eprint = {2306.06475},
+ eprinttype = {arxiv},
+ eprintclass = {cond-mat, physics:physics},
+ doi = {10.48550/arXiv.2306.06475},
+ url = {http://arxiv.org/abs/2306.06475},
+ urldate = {2023-06-26},
+ abstract = {Machine-learned interatomic potentials are fast becoming an indispensable tool in computational materials science. One approach is the ephemeral data-derived potential (EDDP), which was designed to accelerate atomistic structure prediction. The EDDP is simple and cost-efficient. It relies on training data generated in small unit cells and is fit using a lightweight neural network, leading to smooth interactions which exhibit the robust transferability essential for structure prediction. Here, we present a variety of applications of EDDPs, enabled by recent developments of the open-source EDDP software. New features include interfaces to phonon and molecular dynamics codes, as well as deployment of the ensemble deviation for estimating the confidence in EDDP predictions. Through case studies ranging from elemental carbon and lead to the binary scandium hydride and the ternary zinc cyanide, we demonstrate that EDDPs can be trained to cover wide ranges of pressures and stoichiometries, and used to evaluate phonons, phase diagrams, superionicity, and thermal expansion. These developments complement continued success in accelerated structure prediction.},
+ pubstate = {preprint},
+ keywords = {AML,CASTEP,DFT,ensemble learning,HDNNP,Julia,Lennard-Jones,library,materials,ML,MLP,prediction of energy,PW,structure relaxation,with-code,workflows},
+ file = {/Users/wasmer/Nextcloud/Zotero/Salzbrenner et al_2023_Developments and Further Applications of Ephemeral Data Derived Potentials.pdf;/Users/wasmer/Zotero/storage/FAY5AQLM/2306.html}
+}
+
@unpublished{samuelMachineLearningPipelines2020,
title = {Machine {{Learning Pipelines}}: {{Provenance}}, {{Reproducibility}} and {{FAIR Data Principles}}},
shorttitle = {Machine {{Learning Pipelines}}},
@@ -7720,6 +9381,45 @@
file = {/Users/wasmer/Nextcloud/Zotero/Samuel et al_2020_Machine Learning Pipelines.pdf;/Users/wasmer/Zotero/storage/GTJD4NAB/2006.html}
}
+@article{sanchez-lengelingGentleIntroductionGraph2021,
+ title = {A {{Gentle Introduction}} to {{Graph Neural Networks}}},
+ author = {Sanchez-Lengeling, Benjamin and Reif, Emily and Pearce, Adam and Wiltschko, Alexander B.},
+ date = {2021-09-02},
+ journaltitle = {Distill},
+ shortjournal = {Distill},
+ volume = {6},
+ number = {9},
+ pages = {e33},
+ issn = {2476-0757},
+ doi = {10.23915/distill.00033},
+ url = {https://distill.pub/2021/gnn-intro},
+ urldate = {2023-06-28},
+ abstract = {What components are needed for building learning algorithms that leverage the structure and properties of graphs?},
+ langid = {english},
+ keywords = {/unread,AML,blog,General ML,GNN,graph ML,introduction,learning material,ML,MPNN},
+ file = {/Users/wasmer/Zotero/storage/ATECBI8U/gnn-intro.html}
+}
+
+@article{sanvitoMolecularSpintronics2011,
+ title = {Molecular Spintronics},
+ author = {Sanvito, Stefano},
+ date = {2011-05-23},
+ journaltitle = {Chemical Society Reviews},
+ shortjournal = {Chem. Soc. Rev.},
+ volume = {40},
+ number = {6},
+ pages = {3336--3355},
+ publisher = {{The Royal Society of Chemistry}},
+ issn = {1460-4744},
+ doi = {10.1039/C1CS15047B},
+ url = {https://pubs.rsc.org/en/content/articlelanding/2011/cs/c1cs15047b},
+ urldate = {2023-06-30},
+ abstract = {The electron spin made its debut in the device world only two decades ago but today our ability of detecting the spin state of a moving electron underpins the entire magnetic data storage industry. This technological revolution has been driven by a constant improvement in our understanding on how spins can be injected, manipulated and detected in the solid state, a field which is collectively named Spintronics. Recently a number of pioneering experiments and theoretical works suggest that organic materials can offer similar and perhaps superior performances in making spin-devices than the more conventional inorganic metals and semiconductors. Furthermore they can pave the way for radically new device concepts. This is Molecular Spintronics, a blossoming research area aimed at exploring how the unique properties of the organic world can marry the requirements of spin-devices. Importantly, after a first phase, where most of the research was focussed on exporting the concepts of inorganic spintronics to organic materials, the field has moved to a more mature age, where the exploitation of the unique properties of molecules has begun to emerge. Molecular spintronics now collects a diverse and interdisciplinary community ranging from device physicists to synthetic chemists to surface scientists. In this critical review, I will survey this fascinating, rapidly evolving, field with a particular eye on new directions and opportunities. The main differences and challenges with respect to standard spintronics will be discussed and so will be the potential cross-fertilization with other fields (177 references).},
+ langid = {english},
+ keywords = {magnetism,molecular spintronics,molecules,organic chemistry,review,spintronics},
+ file = {/Users/wasmer/Nextcloud/Zotero/Sanvito_2011_Molecular spintronics.pdf}
+}
+
@inproceedings{satorrasEquivariantGraphNeural2021,
title = {E(n) {{Equivariant Graph Neural Networks}}},
booktitle = {Proceedings of the 38th {{International Conference}} on {{Machine Learning}}},
@@ -7786,7 +9486,7 @@
urldate = {2023-04-13},
abstract = {In this study, we introduce an automatic training protocol for developing machine learning force fields (MLFFs) that can accurately determine reaction barriers for a given catalytic reaction pathway. The protocol is demonstrated through its application to the eleven-step hydrogenation of carbon dioxide to methanol over an indium oxide catalyst. The training set is iteratively expanded with active learning, using the model's uncertainty estimates to sample novel configurations that are chemically relevant. Our final force field obtains reaction barriers that are within 0.05 eV of those obtained through Density Functional Theory (DFT) calculations. Additionally, we examine two extrapolation tasks. Firstly, we demonstrate that with only a few extra single-point DFT calculations, we can accurately capture the adsorption energy for all eleven reaction intermediates on platinum-doped surfaces. Secondly, we show that MLFFs can be used to identify a wide range of low-energy adsorption configurations that are thermodynamically relevant. This abundance of adsorbate geometries highlights the need for fast and accurate alternatives to direct ab-initio simulations.},
pubstate = {preprint},
- keywords = {active learning,active learning protocol,AML,chemistry,GAP,Gaussian process,GPR,iterative learning scheme,MACE,ML,ML-FF,MLP,SOAP},
+ keywords = {\_tablet,active learning,active learning protocol,AML,chemistry,GAP,Gaussian process,GPR,iterative learning scheme,MACE,ML,ML-FF,MLP,SOAP},
file = {/Users/wasmer/Nextcloud/Zotero/Schaaf et al_2023_Accurate Reaction Barriers for Catalytic Pathways.pdf;/Users/wasmer/Zotero/storage/LACJS8K5/2301.html}
}
@@ -7806,6 +9506,27 @@
file = {/Users/wasmer/Nextcloud/Zotero/Schaarschmidt et al_2022_Learned Force Fields Are Ready For Ground State Catalyst Discovery.pdf;/Users/wasmer/Zotero/storage/8QZN3D56/2209.html}
}
+@article{schattauerMachineLearningSparse2022,
+ title = {Machine Learning Sparse Tight-Binding Parameters for Defects},
+ author = {Schattauer, Christoph and Todorović, Milica and Ghosh, Kunal and Rinke, Patrick and Libisch, Florian},
+ date = {2022-05-20},
+ journaltitle = {npj Computational Materials},
+ shortjournal = {npj Comput Mater},
+ volume = {8},
+ number = {1},
+ pages = {1--11},
+ publisher = {{Nature Publishing Group}},
+ issn = {2057-3960},
+ doi = {10.1038/s41524-022-00791-x},
+ url = {https://www.nature.com/articles/s41524-022-00791-x},
+ urldate = {2023-07-01},
+ abstract = {We employ machine learning to derive tight-binding parametrizations for the electronic structure of defects. We test several machine learning methods that map the atomic and electronic structure of a defect onto a sparse tight-binding parameterization. Since Multi-layer perceptrons (i.e., feed-forward neural networks) perform best we adopt them for our further investigations. We demonstrate the accuracy of our parameterizations for a range of important electronic structure properties such as band structure, local density of states, transport and level spacing simulations for two common defects in single layer graphene. Our machine learning approach achieves results comparable to maximally localized Wannier functions (i.e., DFT accuracy) without prior knowledge about the electronic structure of the defects while also allowing for a reduced interaction range which substantially reduces calculation time. It is general and can be applied to a wide range of other materials, enabling accurate large-scale simulations of material properties in the presence of different defects.},
+ issue = {1},
+ langid = {english},
+ keywords = {/unread,Electronic properties and devices,Electronic properties and materials,Electronic structure},
+ file = {/Users/wasmer/Nextcloud/Zotero/Schattauer et al_2022_Machine learning sparse tight-binding parameters for defects.pdf}
+}
+
@online{scherbelaFoundationModelNeural2023,
title = {Towards a {{Foundation Model}} for {{Neural Network Wavefunctions}}},
author = {Scherbela, Michael and Gerard, Leon and Grohs, Philipp},
@@ -7982,7 +9703,7 @@
url = {https://doi.org/10.1021/acs.nanolett.1c04055},
urldate = {2022-04-16},
abstract = {The integration of semiconductor Josephson junctions (JJs) in superconducting quantum circuits provides a versatile platform for hybrid qubits and offers a powerful way to probe exotic quasiparticle excitations. Recent proposals for using circuit quantum electrodynamics (cQED) to detect topological superconductivity motivate the integration of novel topological materials in such circuits. Here, we report on the realization of superconducting transmon qubits implemented with (Bi0.06Sb0.94)2Te3 topological insulator (TI) JJs using ultrahigh vacuum fabrication techniques. Microwave losses on our substrates, which host monolithically integrated hardmasks used for the selective area growth of TI nanostructures, imply microsecond limits to relaxation times and, thus, their compatibility with strong-coupling cQED. We use the cavity–qubit interaction to show that the Josephson energy of TI-based transmons scales with their JJ dimensions and demonstrate qubit control as well as temporal quantum coherence. Our results pave the way for advanced investigations of topological materials in both novel Josephson and topological qubits.},
- keywords = {_tablet,experimental,FZJ,PGI,PGI-9,quantum computing,superconductor,topological insulator},
+ keywords = {\_tablet,experimental,FZJ,PGI,PGI-9,quantum computing,superconductor,topological insulator},
file = {/Users/wasmer/Nextcloud/Zotero/Schmitt et al_2022_Integration of Topological Insulator Josephson Junctions in Superconducting.pdf;/Users/wasmer/Zotero/storage/ZVNVRDSF/acs.nanolett.html}
}
@@ -8056,7 +9777,7 @@
urldate = {2022-12-27},
abstract = {SchNetPack is a versatile neural networks toolbox that addresses both the requirements of method development and application of atomistic machine learning. Version 2.0 comes with an improved data pipeline, modules for equivariant neural networks as well as a PyTorch implementation of molecular dynamics. An optional integration with PyTorch Lightning and the Hydra configuration framework powers a flexible command-line interface. This makes SchNetPack 2.0 easily extendable with custom code and ready for complex training task such as generation of 3d molecular structures.},
pubstate = {preprint},
- keywords = {_tablet,Deep learning,equivariant,Hydra,library,MLP,models,PAiNN,pytorch,SchNet,SO(3),with-code},
+ keywords = {\_tablet,Deep learning,equivariant,Hydra,library,MLP,models,PAiNN,pytorch,SchNet,SO(3),with-code},
file = {/Users/wasmer/Nextcloud/Zotero/Schütt et al_2022_SchNetPack 2.pdf;/Users/wasmer/Zotero/storage/AHBKQSBM/2212.html}
}
@@ -8081,9 +9802,10 @@
file = {/Users/wasmer/Nextcloud/Zotero/Schütt et al_2019_Unifying machine learning and quantum chemistry with a deep neural network for.pdf;/Users/wasmer/Zotero/storage/ADRZDHRZ/s41467-019-12875-2.html}
}
-@article{sendekMachineLearningModeling,
+@article{sendekMachineLearningModeling2022,
title = {Machine {{Learning Modeling}} for {{Accelerated Battery Materials Design}} in the {{Small Data Regime}}},
author = {Sendek, Austin D. and Ransom, Brandi and Cubuk, Ekin D. and Pellouchoud, Lenson A. and Nanda, Jagjit and Reed, Evan J.},
+ date = {2022-08-18},
journaltitle = {Advanced Energy Materials},
volume = {n/a},
number = {n/a},
@@ -8094,7 +9816,7 @@
urldate = {2022-07-03},
abstract = {Machine learning (ML)-based approaches to battery design are relatively new but demonstrate significant promise for accelerating the timeline for new materials discovery, process optimization, and cell lifetime prediction. Battery modeling represents an interesting and unconventional application area for ML, as datasets are often small but some degree of physical understanding of the underlying processes may exist. This review article provides discussion and analysis of several important and increasingly common questions: how ML-based battery modeling works, how much data are required, how to judge model performance, and recommendations for building models in the small data regime. This article begins with an introduction to ML in general, highlighting several important concepts for small data applications. Previous ionic conductivity modeling efforts are discussed in depth as a case study to illustrate these modeling concepts. Finally, an overview of modeling efforts in major areas of battery design is provided and several areas for promising future efforts are identified, within the context of typical small data constraints.},
langid = {english},
- keywords = {_tablet,chemistry,materials informatics,ML,small data,tutorial},
+ keywords = {\_tablet,chemistry,materials informatics,ML,small data,tutorial},
file = {/Users/wasmer/Nextcloud/Zotero/Sendek et al_Machine Learning Modeling for Accelerated Battery Materials Design in the Small.pdf;/Users/wasmer/Zotero/storage/55KE647F/aenm.html}
}
@@ -8128,7 +9850,7 @@
urldate = {2023-04-04},
abstract = {In this paper I propose a new model for representing the formation energies of multicomponent crystalline alloys as a function of atom types. In the cases when displacements of atoms from their equilibrium positions are not large, the proposed method has a similar accuracy as the state-of-the-art cluster expansion method, and a better accuracy when the fitting dataset size is small. The proposed model has only two tunable parameters—one for the interaction range and one for the interaction complexity.},
langid = {english},
- keywords = {alloys,AML,cluster expansion,high-entropy alloys,LRP,ML,MLP,n-ary alloys,original publication,prediction of energy,transition metals},
+ keywords = {\_tablet,alloys,AML,cluster expansion,high-entropy alloys,LRP,ML,MLP,n-ary alloys,original publication,prediction of energy,transition metals},
file = {/Users/wasmer/Nextcloud/Zotero/Shapeev_2017_Accurate representation of formation energies of crystalline alloys with many.pdf;/Users/wasmer/Zotero/storage/EQYE3F3F/S0927025617303610.html}
}
@@ -8144,10 +9866,44 @@
urldate = {2022-12-31},
abstract = {In addition to being the core quantity in density functional theory, the charge density can be used in many tertiary analyses in materials sciences from bonding to assigning charge to specific atoms. The charge density is data-rich since it contains information about all the electrons in the system. With increasing utilization of machine-learning tools in materials sciences, a data-rich object like the charge density can be utilized in a wide range of applications. The database presented here provides a modern and user-friendly interface for a large and continuously updated collection of charge densities as part of the Materials Project. In addition to the charge density data, we provide the theory and code for changing the representation of the charge density which should enable more advanced machine-learning studies for the broader community.},
pubstate = {preprint},
- keywords = {_tablet,charge density,data repositories,Database,dimensionality reduction of target,electronic structure,grid-based descriptors,library,materials,materials database,materials project,ML,ML-DFT,prediction from density,prediction of electron density,pseudopotential,representation of density,VASP,with-code},
+ keywords = {\_tablet,charge density,data repositories,Database,dimensionality reduction of target,electronic structure,grid-based descriptors,library,materials,materials database,materials project,ML,ML-DFT,prediction from density,prediction of electron density,pseudopotential,representation of density,VASP,with-code},
file = {/Users/wasmer/Nextcloud/Zotero/Shen et al_2021_A representation-independent electronic charge density database for crystalline.pdf;/Users/wasmer/Zotero/storage/9A3MUVVK/2107.html}
}
+@online{shenRotationEquivariantOperators2022,
+ title = {Rotation {{Equivariant Operators}} for {{Machine Learning}} on {{Scalar}} and {{Vector Fields}}},
+ author = {Shen, Paul and Herbst, Michael and Viswanathan, Venkat},
+ date = {2022-08-04},
+ eprint = {2108.09541},
+ eprinttype = {arxiv},
+ eprintclass = {cs},
+ doi = {10.48550/arXiv.2108.09541},
+ url = {http://arxiv.org/abs/2108.09541},
+ urldate = {2023-05-26},
+ abstract = {We develop theory and software for rotation equivariant operators on scalar and vector fields, with diverse applications in simulation, optimization and machine learning. Rotation equivariance (covariance) means all fields in the system rotate together, implying spatially invariant dynamics that preserve symmetry. Extending the convolution theorems of linear time invariant systems, we theorize that linear equivariant operators are characterized by tensor field convolutions using an appropriate product between the input field and a radially symmetric kernel field. Most Green's functions and differential operators are in fact equivariant operators, which can also fit unknown symmetry preserving dynamics by parameterizing the radial function. We implement the Julia package EquivariantOperators.jl for fully differentiable finite difference equivariant operators on scalar, vector and higher order tensor fields in 2d/3d. It can run forwards for simulation or image processing, or be back propagated for computer vision, inverse problems and optimal control. Code at https://aced-differentiate.github.io/EquivariantOperators.jl/},
+ pubstate = {preprint},
+ keywords = {AML,CNN,covariant,differential operator,electric charge,Electric field,electric potential,equivariant,equivariant operator,finite differences,General ML,Green’s Function Method,grid-based descriptors,image classification,Julia,library,ML,ML-DFA,ML-DFT,ML-ESM,neural nets,nonuniform grid,operator,PDE,PINN,prediction from density,prediction of,prediction of Exc,scalar field,tensor field,tensorial target,vector field,vectorial learning target,with-code},
+ file = {/Users/wasmer/Nextcloud/Zotero/Shen et al_2022_Rotation Equivariant Operators for Machine Learning on Scalar and Vector Fields.pdf;/Users/wasmer/Zotero/storage/NYTWYPMA/2108.html}
+}
+
+@article{shibaClassicalSpinsSuperconductors1968,
+ title = {Classical {{Spins}} in {{Superconductors}}},
+ author = {Shiba, Hiroyuki},
+ date = {1968-09-01},
+ journaltitle = {Progress of Theoretical Physics},
+ shortjournal = {Progress of Theoretical Physics},
+ volume = {40},
+ number = {3},
+ pages = {435--451},
+ issn = {0033-068X},
+ doi = {10.1143/PTP.40.435},
+ url = {https://doi.org/10.1143/PTP.40.435},
+ urldate = {2023-05-10},
+ abstract = {It is shown that there exists a localized excited state in the energy gap in a superconductor with a classical spin. At finite concentration localized excited states around classical spins form an “impurity band”. The process of growth of the "impurity band" and its effects on observable quantities are investigated.},
+ keywords = {/unread,original publication,physics,rec-by-da-silva,spin-dependent,superconductor,Yu-Shiba-Rusinov,Yu-Shiba-Rusinov state},
+ file = {/Users/wasmer/Nextcloud/Zotero/Shiba_1968_Classical Spins in Superconductors.pdf;/Users/wasmer/Zotero/storage/DVLUPVC7/1831894.html}
+}
+
@article{shmilovichOrbitalMixerUsing2022,
title = {Orbital {{Mixer}}: {{Using Atomic Orbital Features}} for {{Basis-Dependent Prediction}} of {{Molecular Wavefunctions}}},
shorttitle = {Orbital {{Mixer}}},
@@ -8161,7 +9917,7 @@
url = {https://doi.org/10.1021/acs.jctc.2c00555},
urldate = {2022-09-29},
abstract = {Leveraging ab initio data at scale has enabled the development of machine learning models capable of extremely accurate and fast molecular property prediction. A central paradigm of many previous studies focuses on generating predictions for only a fixed set of properties. Recent lines of research instead aim to explicitly learn the electronic structure via molecular wavefunctions, from which other quantum chemical properties can be directly derived. While previous methods generate predictions as a function of only the atomic configuration, in this work we present an alternate approach that directly purposes basis-dependent information to predict molecular electronic structure. Our model, Orbital Mixer, is composed entirely of multi-layer perceptrons (MLPs) using MLP-Mixer layers within a simple, intuitive, and scalable architecture that achieves competitive Hamiltonian and molecular orbital energy and coefficient prediction accuracies compared to the state-of-the-art.},
- keywords = {_tablet,ML,ML-ESM,MLP,molecules,Orbital Mixer,original publication,PhiSNet,prediction of wavefunction,SchNOrb,with-code},
+ keywords = {\_tablet,ML,ML-ESM,MLP,molecules,Orbital Mixer,original publication,PhiSNet,prediction of wavefunction,SchNOrb,with-code},
file = {/Users/wasmer/Nextcloud/Zotero/Shmilovich et al_2022_Orbital Mixer.pdf}
}
@@ -8182,6 +9938,25 @@
file = {/Users/wasmer/Nextcloud/Zotero/Simmhan et al_2005_A survey of data provenance in e-science.pdf}
}
+@article{singhRareearthBasedHalfHeusler2020,
+ title = {Rare-Earth Based Half-{{Heusler}} Topological Quantum Materials: {{A}} Perspective},
+ shorttitle = {Rare-Earth Based Half-{{Heusler}} Topological Quantum Materials},
+ author = {Singh, Ashutosh Kumar and Ramarao, S. D. and Peter, Sebastian C.},
+ date = {2020-06-24},
+ journaltitle = {APL Materials},
+ shortjournal = {APL Materials},
+ volume = {8},
+ number = {6},
+ pages = {060903},
+ issn = {2166-532X},
+ doi = {10.1063/5.0006118},
+ url = {https://doi.org/10.1063/5.0006118},
+ urldate = {2023-06-15},
+ abstract = {Topological insulator (TI) materials which are conductive at the surface but insulating in the bulk have drawn much attention in the past decade due to their fascinating properties and potential application in the field of spintronics, quantum computing, topological superconductivity and next generation electronics. In the search of three-dimensional TIs, half-Heusler compounds are the new entrants. Half-Heusler compounds are equiatomic ternary compounds with cubic symmetry. Due to the availability of a large pool of elements in the half Heusler family, the physical properties of these materials can be tuned by choosing the desired combination of elements. In this perspective, we have briefly discussed the development of structural relations, the quantum hall effect, Landau quantization, and topological properties of a few representative systems in the half-Heusler family, including methods by which they are studied and characterized such as Angle Resolved Photoemission Spectroscopy, Shubnikov-de-Hass Oscillations and Nuclear Magnetic Resonance.},
+ keywords = {/unread,Heusler,perspective,rare earths,topological,topological insulator},
+ file = {/Users/wasmer/Nextcloud/Zotero/Singh et al_2020_Rare-earth based half-Heusler topological quantum materials.pdf;/Users/wasmer/Zotero/storage/K3TVA8HX/Rare-earth-based-half-Heusler-topological-quantum.html}
+}
+
@article{singraberParallelMultistreamTraining2019,
title = {Parallel {{Multistream Training}} of {{High-Dimensional Neural Network Potentials}}},
author = {Singraber, Andreas and Morawietz, Tobias and Behler, Jörg and Dellago, Christoph},
@@ -8272,7 +10047,7 @@
url = {https://link.aps.org/doi/10.1103/PhysRevLett.108.253002},
urldate = {2021-10-15},
abstract = {Machine learning is used to approximate density functionals. For the model problem of the kinetic energy of noninteracting fermions in 1D, mean absolute errors below 1 kcal/mol on test densities similar to the training set are reached with fewer than 100 training densities. A predictor identifies if a test density is within the interpolation region. Via principal component analysis, a projected functional derivative finds highly accurate self-consistent densities. The challenges for application of our method to real electronic structure problems are discussed.},
- keywords = {_tablet,2-step model,DFT,dimensionality reduction,KRR,ML,ML-DFA,ML-DFT,ML-ESM,ML-OF,models,orbital-free DFT,original publication,PCA,prediction from density,prediction from density functional,prediction of electron density,prediction of kinetic energy},
+ keywords = {\_tablet,2-step model,DFT,dimensionality reduction,KRR,ML,ML-DFA,ML-DFT,ML-ESM,ML-OF,models,orbital-free DFT,original publication,PCA,prediction from density,prediction from density functional,prediction of electron density,prediction of kinetic energy},
file = {/Users/wasmer/Nextcloud/Zotero/Snyder et al_2012_Finding Density Functionals with Machine Learning.pdf;/Users/wasmer/Zotero/storage/6NMNCTQB/Snyder et al. - 2012 - Finding Density Functionals with Machine Learning.tex;/Users/wasmer/Zotero/storage/TBZPF93I/Snyder et al_2012_Finding Density Functionals with Machine Learning.pdf;/Users/wasmer/Zotero/storage/RRS5SC4P/PhysRevLett.108.html}
}
@@ -8410,6 +10185,34 @@
file = {/Users/wasmer/Nextcloud/Zotero/Steinbach_2022_Reproducibility in Data Science and Machine Learning.pdf;/Users/wasmer/Zotero/storage/MPANKYEK/1.html}
}
+@online{sternCourseCampusTopological2023,
+ type = {MOOC},
+ title = {Course - {{Campus IL}} - {{Topological Quantum Matter}} 2023},
+ shorttitle = {Topological {{Quantum Matter}}},
+ author = {Stern, Ady and Beidenkopf, Haim and Berg, Erez and Oreg, Yuval},
+ date = {2023},
+ url = {https://campus.gov.il/en/course/weizmann-acd-quantum-topologicalstatesofmatter-en},
+ urldate = {2023-06-16},
+ abstract = {In the theoretical and experimental studies of topological states of matter, including the quantum Hall effect; topological insulators, superconductors, and semimetals; twisted bilayer graphene. The course covers advanced theoretical and experimental methods},
+ langid = {american},
+ organization = {{קמפוס IL}},
+ keywords = {/unread},
+ file = {/Users/wasmer/Zotero/storage/PQ5256JG/weizmann-acd-quantum-topologicalstatesofmatter-en.html}
+}
+
+@report{stevensAIScienceReport2020,
+ title = {{{AI}} for {{Science Report}} 2020},
+ author = {Stevens, Rick and Taylor, Valerie and Nichols, Jeff and Maccabe, Arthur B. and Yelick, Katherine and Brown, David},
+ date = {2020},
+ institution = {{DOE Office of Science}},
+ url = {https://www.anl.gov/cels/reference/ai-for-science-report-2020},
+ urldate = {2023-06-28},
+ abstract = {Argonne, Oak Ridge, and Berkeley national laboratories hosted four AI for Science town halls attended by more than a thousand scientists and engineers from the U.S. Department of Energy (DOE) national laboratories. The goal of the town hall series was to examine scientific opportunities in the areas of artificial intelligence (AI), big data, and high-performance computing (HPC) in the next decade, and to capture the big ideas, grand challenges, and next steps to realizing these opportunities. Sixteen topical expert teams summarized the state of the art, outlined challenges, developed an AI roadmap for the coming decade, and explored opportunities for accelerating progress on that roadmap. Following the town halls, an AI for Science Report was compiled, which captures and highlights the important themes that emerged for AI applications in science and outlines the research and infrastructure needed to advance AI methods and techniques for science applications.},
+ langid = {english},
+ keywords = {AI,DOE,HPC,ML,report},
+ file = {/Users/wasmer/Nextcloud/Zotero/Stevens et al_2020_AI for Science Report 2020.pdf;/Users/wasmer/Zotero/storage/Q2KH2Y8A/ai-for-science-report-2020.html}
+}
+
@article{suttonIdentifyingDomainsApplicability2020,
title = {Identifying Domains of Applicability of Machine Learning Models for Materials Science},
author = {Sutton, Christopher and Boley, Mario and Ghiringhelli, Luca M. and Rupp, Matthias and Vreeken, Jilles and Scheffler, Matthias},
@@ -8448,6 +10251,64 @@
file = {/Users/wasmer/Nextcloud/Zotero/Szlachta et al_2014_Accuracy and transferability of Gaussian approximation potential models for.pdf;/Users/wasmer/Nextcloud/Zotero/Szlachta et al_2014_Accuracy and transferability of Gaussian approximation potential models for2.pdf;/Users/wasmer/Zotero/storage/YFHICPLQ/PhysRevB.90.html}
}
+@article{takamotoTeaNetUniversalNeural2022,
+ title = {{{TeaNet}}: {{Universal}} Neural Network Interatomic Potential Inspired by Iterative Electronic Relaxations},
+ shorttitle = {{{TeaNet}}},
+ author = {Takamoto, So and Izumi, Satoshi and Li, Ju},
+ date = {2022-05-01},
+ journaltitle = {Computational Materials Science},
+ shortjournal = {Computational Materials Science},
+ volume = {207},
+ pages = {111280},
+ issn = {0927-0256},
+ doi = {10.1016/j.commatsci.2022.111280},
+ url = {https://www.sciencedirect.com/science/article/pii/S0927025622000799},
+ urldate = {2023-06-30},
+ abstract = {A universal interatomic potential for an arbitrary set of chemical elements is urgently needed in computational materials science. Graph convolution neural network (GCN) has rich expressive power, but previously was mainly employed to transport scalars and vectors, not rank ≥2 tensors. As classic interatomic potentials were inspired by tight-binding electronic relaxation framework, we want to represent this iterative propagation of rank ≥2 tensor information by GCN. Here we propose an architecture called the tensor embedded atom network (TeaNet) where angular interaction is translated into graph convolution through the incorporation of Euclidean tensors, vectors and scalars. By applying the residual network (ResNet) architecture and training with recurrent GCN weights initialization, a much deeper (16 layers) GCN was constructed, whose flow is similar to an iterative electronic relaxation. Our training dataset is generated by density functional theory calculation of mostly chemically and structurally randomized configurations. We demonstrate that arbitrary structures and reactions involving the first 18 elements on the periodic table (H to Ar) can be realized satisfactorily by TeaNet, including C–H molecular structures, metals, amorphous SiO2, and water, showing surprisingly good performance (energy mean absolute error 19 meV/atom) and robustness for arbitrary chemistries involving elements from H to Ar.},
+ langid = {english},
+ keywords = {/unread,AML,GNN,ML,MLP,TeaNet,tensorial target,universal potential},
+ file = {/Users/wasmer/Nextcloud/Zotero/Takamoto et al_2022_TeaNet.pdf;/Users/wasmer/Zotero/storage/BF944L4Z/S0927025622000799.html}
+}
+
+@article{takamotoUniversalNeuralNetwork2022,
+ title = {Towards Universal Neural Network Potential for Material Discovery Applicable to Arbitrary Combination of 45 Elements},
+ author = {Takamoto, So and Shinagawa, Chikashi and Motoki, Daisuke and Nakago, Kosuke and Li, Wenwen and Kurata, Iori and Watanabe, Taku and Yayama, Yoshihiro and Iriguchi, Hiroki and Asano, Yusuke and Onodera, Tasuku and Ishii, Takafumi and Kudo, Takao and Ono, Hideki and Sawada, Ryohto and Ishitani, Ryuichiro and Ong, Marc and Yamaguchi, Taiki and Kataoka, Toshiki and Hayashi, Akihide and Charoenphakdee, Nontawat and Ibuka, Takeshi},
+ date = {2022-05-30},
+ journaltitle = {Nature Communications},
+ shortjournal = {Nat Commun},
+ volume = {13},
+ number = {1},
+ pages = {2991},
+ publisher = {{Nature Publishing Group}},
+ issn = {2041-1723},
+ doi = {10.1038/s41467-022-30687-9},
+ url = {https://www.nature.com/articles/s41467-022-30687-9},
+ urldate = {2023-06-30},
+ abstract = {Computational material discovery is under intense study owing to its ability to explore the vast space of chemical systems. Neural network potentials (NNPs) have been shown to be particularly effective in conducting atomistic simulations for such purposes. However, existing NNPs are generally designed for narrow target materials, making them unsuitable for broader applications in material discovery. Here we report a development of universal NNP called PreFerred Potential (PFP), which is able to handle any combination of 45 elements. Particular emphasis is placed on the datasets, which include a diverse set of virtual structures used to attain the universality. We demonstrated the applicability of PFP in selected domains: lithium diffusion in LiFeSO4F, molecular adsorption in metal-organic frameworks, an order–disorder transition of Cu-Au alloys, and material discovery for a Fischer–Tropsch catalyst. They showcase the power of PFP, and this technology provides a highly useful tool for material discovery.},
+ issue = {1},
+ langid = {english},
+ keywords = {/unread,AML,ML,MLP,PreFerred Potential,universal potential},
+ file = {/Users/wasmer/Nextcloud/Zotero/Takamoto et al_2022_Towards universal neural network potential for material discovery applicable to.pdf}
+}
+
+@article{takamotoUniversalNeuralNetwork2023,
+ title = {Towards Universal Neural Network Interatomic Potential},
+ author = {Takamoto, So and Okanohara, Daisuke and Li, Qing-Jie and Li, Ju},
+ date = {2023-05-01},
+ journaltitle = {Journal of Materiomics},
+ shortjournal = {Journal of Materiomics},
+ volume = {9},
+ number = {3},
+ pages = {447--454},
+ issn = {2352-8478},
+ doi = {10.1016/j.jmat.2022.12.007},
+ url = {https://www.sciencedirect.com/science/article/pii/S2352847823000072},
+ urldate = {2023-06-30},
+ langid = {english},
+ keywords = {AML,GNN,ML,MLP,prediction of electron density,PreFerred Potential,TeaNet,tensorial target,universal potential},
+ file = {/Users/wasmer/Nextcloud/Zotero/Takamoto et al_2023_Towards universal neural network interatomic potential.pdf;/Users/wasmer/Zotero/storage/XEJZUGIH/S2352847823000072.html}
+}
+
@article{talirzMaterialsCloudPlatform2020,
title = {Materials {{Cloud}}, a Platform for Open Computational Science},
author = {Talirz, Leopold and Kumbhar, Snehal and Passaro, Elsa and Yakutovich, Aliaksandr V. and Granata, Valeria and Gargiulo, Fernando and Borelli, Marco and Uhrin, Martin and Huber, Sebastiaan P. and Zoupanos, Spyros and Adorf, Carl S. and Andersen, Casper Welzel and Schütt, Ole and Pignedoli, Carlo A. and Passerone, Daniele and VandeVondele, Joost and Schulthess, Thomas C. and Smit, Berend and Pizzi, Giovanni and Marzari, Nicola},
@@ -8483,6 +10344,25 @@
file = {/Users/wasmer/Nextcloud/Zotero/Talirz et al_2021_Trends in atomistic simulation software usage.pdf;/Users/wasmer/Zotero/storage/SCDVYPXG/2108.html}
}
+@article{talirzTrendsAtomisticSimulation2021a,
+ title = {Trends in {{Atomistic Simulation Software Usage}} [{{Article}} v1.0]},
+ author = {Talirz, Leopold and Ghiringhelli, Luca M. and Smit, Berend},
+ date = {2021-10-25},
+ journaltitle = {Living Journal of Computational Molecular Science},
+ volume = {3},
+ number = {1},
+ pages = {1483--1483},
+ issn = {2575-6524},
+ doi = {10.33011/livecoms.3.1.1483},
+ url = {https://livecomsjournal.org/index.php/livecoms/article/view/v3i1e1483},
+ urldate = {2023-06-19},
+ abstract = {Driven by the unprecedented computational power available to scientific research, the use of computers in solid-state physics, chemistry and materials science has been on a continuous rise. This review focuses on the software used for the simulation of matter at the atomic scale. We provide a comprehensive overview of major codes in the field, and analyze how citations to these codes in the academic literature have evolved since 2010. An interactive version of the underlying data set is available at https://atomistic.software.},
+ issue = {1},
+ langid = {english},
+ keywords = {/unread,DFT,lists,software},
+ file = {/Users/wasmer/Nextcloud/Zotero/Talirz et al_2021_Trends in Atomistic Simulation Software Usage [Article v1.pdf}
+}
+
@article{tealeDFTExchangeSharing2022,
title = {{{DFT Exchange}}: {{Sharing Perspectives}} on the {{Workhorse}} of {{Quantum Chemistry}} and {{Materials Science}}},
shorttitle = {{{DFT Exchange}}},
@@ -8514,6 +10394,19 @@
file = {/Users/wasmer/Nextcloud/Zotero/Teufel et al_2022_MEGAN.pdf;/Users/wasmer/Zotero/storage/4ZA3I5AT/2211.html}
}
+@online{teufelQuantifyingIntrinsicUsefulness2023,
+ title = {Quantifying the {{Intrinsic Usefulness}} of {{Attributional Explanations}} for {{Graph Neural Networks}} with {{Artificial Simulatability Studies}}},
+ author = {Teufel, Jonas and Torresi, Luca and Friederich, Pascal},
+ date = {2023-05-25},
+ url = {https://arxiv.org/abs/2305.15961v1},
+ urldate = {2023-07-01},
+ abstract = {Despite the increasing relevance of explainable AI, assessing the quality of explanations remains a challenging issue. Due to the high costs associated with human-subject experiments, various proxy metrics are often used to approximately quantify explanation quality. Generally, one possible interpretation of the quality of an explanation is its inherent value for teaching a related concept to a student. In this work, we extend artificial simulatability studies to the domain of graph neural networks. Instead of costly human trials, we use explanation-supervisable graph neural networks to perform simulatability studies to quantify the inherent usefulness of attributional graph explanations. We perform an extensive ablation study to investigate the conditions under which the proposed analyses are most meaningful. We additionally validate our methods applicability on real-world graph classification and regression datasets. We find that relevant explanations can significantly boost the sample efficiency of graph neural networks and analyze the robustness towards noise and bias in the explanations. We believe that the notion of usefulness obtained from our proposed simulatability analysis provides a dimension of explanation quality that is largely orthogonal to the common practice of faithfulness and has great potential to expand the toolbox of explanation quality assessments, specifically for graph explanations.},
+ langid = {english},
+ organization = {{arXiv.org}},
+ keywords = {/unread},
+ file = {/Users/wasmer/Nextcloud/Zotero/Teufel et al_2023_Quantifying the Intrinsic Usefulness of Attributional Explanations for Graph.pdf}
+}
+
@thesis{thiessDevelopmentApplicationMassively2011,
title = {Development and Application of a Massively Parallel {{KKR Green}} Function Method for Large Scale Systems},
author = {Thieß, Alexander R. and Blügel, Stefan},
@@ -8550,7 +10443,7 @@
volume = {85},
number = {23},
doi = {10.1103/PhysRevB.85.235103},
- keywords = {_tablet,juKKR,KKR,KKRnano,PGI-1/IAS-1},
+ keywords = {\_tablet,juKKR,KKR,KKRnano,PGI-1/IAS-1},
file = {/Users/wasmer/Nextcloud/Zotero/Thiess_2012_Massively parallel density functional calculations for thousands of atoms.pdf;/Users/wasmer/Zotero/storage/PM97ULPL/PhysRevB.85.html}
}
@@ -8564,9 +10457,27 @@
volume = {85},
number = {23},
doi = {10.1103/PhysRevB.85.235103},
+ keywords = {DFT,disordered,HPC,KKR,KKRnano,linear-scaling DFT,Metals and alloys,parallelization},
file = {/Users/wasmer/Nextcloud/Zotero/Thiess_2012_Massively parallel density functional calculations for thousands of atoms2.pdf;/Users/wasmer/Zotero/storage/NJSJUCGL/PhysRevB.85.html}
}
+@online{thomasTensorFieldNetworks2018,
+ title = {Tensor Field Networks: {{Rotation-}} and Translation-Equivariant Neural Networks for {{3D}} Point Clouds},
+ shorttitle = {Tensor Field Networks},
+ author = {Thomas, Nathaniel and Smidt, Tess and Kearnes, Steven and Yang, Lusann and Li, Li and Kohlhoff, Kai and Riley, Patrick},
+ date = {2018-05-18},
+ eprint = {1802.08219},
+ eprinttype = {arxiv},
+ eprintclass = {cs},
+ doi = {10.48550/arXiv.1802.08219},
+ url = {http://arxiv.org/abs/1802.08219},
+ urldate = {2023-06-30},
+ abstract = {We introduce tensor field neural networks, which are locally equivariant to 3D rotations, translations, and permutations of points at every layer. 3D rotation equivariance removes the need for data augmentation to identify features in arbitrary orientations. Our network uses filters built from spherical harmonics; due to the mathematical consequences of this filter choice, each layer accepts as input (and guarantees as output) scalars, vectors, and higher-order tensors, in the geometric sense of these terms. We demonstrate the capabilities of tensor field networks with tasks in geometry, physics, and chemistry.},
+ pubstate = {preprint},
+ keywords = {AML,classical mechanics,continuous convolution,convolution,e3nn,equivariant,General ML,geometric tensors,ML,molecules,point cloud data,shape classification,tensor field,tensorial target,with-code},
+ file = {/Users/wasmer/Nextcloud/Zotero/Thomas et al_2018_Tensor field networks.pdf;/Users/wasmer/Zotero/storage/2U9EVPUR/1802.html}
+}
+
@article{thompsonSpectralNeighborAnalysis2015,
title = {Spectral Neighbor Analysis Method for Automated Generation of Quantum-Accurate Interatomic Potentials},
author = {Thompson, A. P. and Swiler, L. P. and Trott, C. R. and Foiles, S. M. and Tucker, G. J.},
@@ -8600,7 +10511,7 @@
abstract = {We present a new interatomic potential for solids and liquids called Spectral Neighbor Analysis Potential (SNAP). The SNAP potential has a very general form and uses machine-learning techniques to reproduce the energies, forces, and stress tensors of a large set of small configurations of atoms, which are obtained using high-accuracy quantum electronic structure (QM) calculations. The local environment of each atom is characterized by a set of bispectrum components of the local neighbor density projected onto a basis of hyperspherical harmonics in four dimensions. The bispectrum components are the same bond-orientational order parameters employed by the GAP potential [1]. The SNAP potential, unlike GAP, assumes a linear relationship between atom energy and bispectrum components. The linear SNAP coefficients are determined using weighted least-squares linear regression against the full QM training set. This allows the SNAP potential to be fit in a robust, automated manner to large QM data sets using many bispectrum components. The calculation of the bispectrum components and the SNAP potential are implemented in the LAMMPS parallel molecular dynamics code. We demonstrate that a previously unnoticed symmetry property can be exploited to reduce the computational cost of the force calculations by more than one order of magnitude. We present results for a SNAP potential for tantalum, showing that it accurately reproduces a range of commonly calculated properties of both the crystalline solid and the liquid phases. In addition, unlike simpler existing potentials, SNAP correctly predicts the energy barrier for screw dislocation migration in BCC tantalum.},
langid = {english},
keywords = {AML,bispectrum,descriptors,GAP,library,ML,MLP,optimization,original publication,SNAP},
- file = {/Users/wasmer/Nextcloud/Zotero/Thompson et al_2015_Spectral neighbor analysis method for automated generation of quantum-accurate2.pdf;/Users/wasmer/Zotero/storage/DIRR439L/S0021999114008353.html}
+ file = {/Users/wasmer/Nextcloud/Zotero/false;/Users/wasmer/Zotero/storage/DIRR439L/S0021999114008353.html}
}
@unpublished{togoSpglibSoftwareLibrary2018,
@@ -8658,6 +10569,27 @@
abstract = {Skyrmion, a concept originally proposed in particle physics half a century ago, can now find the most fertile field for its applicability, that is, the magnetic skyrmion realized in helimagnetic materials. The spin swirling vortex-like texture of the magnetic skyrmion can define the particle nature by topology; that is, all the constituent spin moments within the two-dimensional sheet wrap the sphere just one time. Such a topological nature of the magnetic skyrmion can lead to extraordinary metastability via topological protection and the driven motion with low electric-current excitation, which may promise future application to spintronics. The skyrmions in the magnetic materials frequently show up as the crystal lattice form, e.g., hexagonal lattice, but sometimes as isolated or independent particles. These skyrmions in magnets were initially found in acentric magnets, such as chiral, polar, and bilayered magnets endowed with antisymmetric spin exchange interaction, while the skyrmion host materials have been explored in a broader family of compounds including centrosymmetric magnets. This review describes the materials science and materials chemistry of magnetic skyrmions using the classification scheme of the skyrmion forming microscopic mechanisms. The emergent phenomena and functions mediated by skyrmions are described, including the generation of emergent magnetic and electric field by statics and dynamics of skrymions and the inherent magnetoelectric effect. The other important magnetic topological defects in two or three dimensions, such as biskyrmions, antiskyrmions, merons, and hedgehogs, are also reviewed in light of their interplay with the skyrmions.}
}
+@article{tokuraMagneticTopologicalInsulators2019,
+ title = {Magnetic Topological Insulators},
+ author = {Tokura, Yoshinori and Yasuda, Kenji and Tsukazaki, Atsushi},
+ date = {2019-02},
+ journaltitle = {Nature Reviews Physics},
+ shortjournal = {Nat Rev Phys},
+ volume = {1},
+ number = {2},
+ pages = {126--143},
+ publisher = {{Nature Publishing Group}},
+ issn = {2522-5820},
+ doi = {10.1038/s42254-018-0011-5},
+ url = {https://www.nature.com/articles/s42254-018-0011-5},
+ urldate = {2023-06-14},
+ abstract = {The importance of global band topology is unequivocally recognized in condensed matter physics, and new states of matter, such as topological insulators, have been discovered. Owing to their bulk band topology, 3D topological insulators possess a massless Dirac dispersion with spin–momentum locking at the surface. Although 3D topological insulators were originally proposed in time-reversal invariant systems, the onset of a spontaneous magnetization or, equivalently, a broken time-reversal symmetry leads to the formation of an exchange gap in the Dirac band dispersion. In such magnetic topological insulators, tuning of the Fermi level in the exchange gap results in the emergence of a quantum Hall effect at zero magnetic field, that is, of a quantum anomalous Hall effect. Here, we review the basic concepts of magnetic topological insulators and their experimental realization, together with the discovery and verification of their emergent properties. In particular, we discuss how the development of tailored materials through heterostructure engineering has made it possible to access the quantum anomalous Hall effect, the topological magnetoelectric effect, the physics related to the chiral edge states that appear in these materials and various spintronic phenomena. Further theoretical and experimental research on magnetic topological insulators will provide fertile ground for the development of new concepts for next-generation electronic devices for applications such as spintronics with low energy consumption, dissipationless topological electronics and topological quantum computation.},
+ issue = {2},
+ langid = {english},
+ keywords = {\_tablet,breaking of TRS,Chern number,Hall effect,Hall QAHE,Hall QHE,heterostructures,magnetic doping,magnetic TIs,Majorana,materials,physics,review,spin-momentum locking,Spintronics,TKNN,topological insulator,transition metals,TRS},
+ file = {/Users/wasmer/Nextcloud/Zotero/Tokura et al_2019_Magnetic topological insulators.pdf}
+}
+
@article{townsendDataDrivenAccelerationCoupledCluster2019,
title = {Data-{{Driven Acceleration}} of the {{Coupled-Cluster Singles}} and {{Doubles Iterative Solver}}},
author = {Townsend, Jacob and Vogiatzis, Konstantinos D.},
@@ -8771,7 +10703,7 @@
url = {https://proceedings.neurips.cc/paper/2021/hash/78f1893678afbeaa90b1fa01b9cfb860-Abstract.html},
urldate = {2022-08-21},
abstract = {Machine learning has enabled the prediction of quantum chemical properties with high accuracy and efficiency, allowing to bypass computationally costly ab initio calculations. Instead of training on a fixed set of properties, more recent approaches attempt to learn the electronic wavefunction (or density) as a central quantity of atomistic systems, from which all other observables can be derived. This is complicated by the fact that wavefunctions transform non-trivially under molecular rotations, which makes them a challenging prediction target. To solve this issue, we introduce general SE(3)-equivariant operations and building blocks for constructing deep learning architectures for geometric point cloud data and apply them to reconstruct wavefunctions of atomistic systems with unprecedented accuracy. Our model achieves speedups of over three orders of magnitude compared to ab initio methods and reduces prediction errors by up to two orders of magnitude compared to the previous state-of-the-art. This accuracy makes it possible to derive properties such as energies and forces directly from the wavefunction in an end-to-end manner. We demonstrate the potential of our approach in a transfer learning application, where a model trained on low accuracy reference wavefunctions implicitly learns to correct for electronic many-body interactions from observables computed at a higher level of theory. Such machine-learned wavefunction surrogates pave the way towards novel semi-empirical methods, offering resolution at an electronic level while drastically decreasing computational cost. Additionally, the predicted wavefunctions can serve as initial guess in conventional ab initio methods, decreasing the number of iterations required to arrive at a converged solution, thus leading to significant speedups without any loss of accuracy or robustness. While we focus on physics applications in this contribution, the proposed equivariant framework for deep learning on point clouds is promising also beyond, say, in computer vision or graphics.},
- keywords = {_tablet,EGNN,equivariant,initial guess,ML-ESM,original publication,PhiSNet,prediction of electron density,prediction of wavefunction,SchNOrb,with-review},
+ keywords = {\_tablet,EGNN,equivariant,initial guess,ML-ESM,original publication,PhiSNet,prediction of electron density,prediction of wavefunction,SchNOrb,with-review},
file = {/Users/wasmer/Nextcloud/Zotero/Unke et al_2021_SE(3)-equivariant prediction of molecular wavefunctions and electronic densities.pdf}
}
@@ -8792,7 +10724,7 @@
abstract = {Atomistic modeling of chemically reactive systems has so far relied on either expensive ab initio methods or bond-order force fields requiring arduous parametrization. Here, we describe a Bayesian active learning framework for autonomous “on-the-fly” training of fast and accurate reactive many-body force fields during molecular dynamics simulations. At each time-step, predictive uncertainties of a sparse Gaussian process are evaluated to automatically determine whether additional ab initio training data are needed. We introduce a general method for mapping trained kernel models onto equivalent polynomial models whose prediction cost is much lower and independent of the training set size. As a demonstration, we perform direct two-phase simulations of heterogeneous H2 turnover on the Pt(111) catalyst surface at chemical accuracy. The model trains itself in three days and performs at twice the speed of a ReaxFF model, while maintaining much higher fidelity to DFT and excellent agreement with experiment.},
issue = {1},
langid = {english},
- keywords = {/unread,active learning,active learning online,AML,Bayesian methods,FLARE,Gaussian process,GPR,iterative learning,library,MD,ML,MLP,uncertainty quantification,with-code},
+ keywords = {\_tablet,/unread,active learning,active learning online,AML,Bayesian methods,FLARE,Gaussian process,GPR,iterative learning,library,MD,ML,MLP,uncertainty quantification,with-code},
file = {/Users/wasmer/Nextcloud/Zotero/Vandermause et al_2022_Active learning of reactive Bayesian force fields applied to heterogeneous.pdf}
}
@@ -8901,6 +10833,21 @@
file = {/Users/wasmer/Nextcloud/Zotero/Vedmedenko et al_2020_The 2020 magnetism roadmap.pdf}
}
+@inproceedings{villarScalarsAreUniversal2021,
+ title = {Scalars Are Universal: {{Equivariant}} Machine Learning, Structured like Classical Physics},
+ shorttitle = {Scalars Are Universal},
+ booktitle = {Advances in {{Neural Information Processing Systems}}},
+ author = {Villar, Soledad and Hogg, David W and Storey-Fisher, Kate and Yao, Weichi and Blum-Smith, Ben},
+ date = {2021},
+ volume = {34},
+ pages = {28848--28863},
+ publisher = {{Curran Associates, Inc.}},
+ url = {https://proceedings.neurips.cc/paper/2021/hash/f1b0775946bc0329b35b823b86eeb5f5-Abstract.html},
+ urldate = {2023-06-30},
+ keywords = {Einstein summation,equivariant,general ML,group theory,invariance,ML,ML theory,Physics ML,symmetry},
+ file = {/Users/wasmer/Nextcloud/Zotero/Villar et al_2021_Scalars are universal.pdf}
+}
+
@article{vojvodicExploringLimitsLowpressure2014,
title = {Exploring the Limits: {{A}} Low-Pressure, Low-Temperature {{Haber}}–{{Bosch}} Process},
shorttitle = {Exploring the Limits},
@@ -8970,7 +10917,7 @@
urldate = {2021-10-19},
abstract = {Accurate approximations to density functionals have recently been obtained via machine learning (ML). By applying ML to a simple function of one variable without any random sampling, we extract the qualitative dependence of errors on hyperparameters. We find universal features of the behavior in extreme limits, including both very small and very large length scales, and the noise-free limit. We show how such features arise in ML models of density functionals. © 2015 Wiley Periodicals, Inc.},
langid = {english},
- keywords = {_tablet,density functional theory,DFT,extreme behaviors,hyperparameters optimization,KRR,machine learning,ML,models,noise-free curve,tutorial},
+ keywords = {\_tablet,density functional theory,DFT,extreme behaviors,hyperparameters optimization,KRR,machine learning,ML,models,noise-free curve,tutorial},
file = {/home/johannes/Nextcloud/Zotero/false;/home/johannes/Nextcloud/Zotero/false;/Users/wasmer/Nextcloud/Zotero/Vu et al_2015_Understanding kernel ridge regression.pdf;/Users/wasmer/Zotero/storage/5INUIEQC/qua.html}
}
@@ -9004,6 +10951,22 @@
file = {/Users/wasmer/Nextcloud/Zotero/Wang et al_2019_Graph Nets for Partial Charge Prediction.pdf;/Users/wasmer/Zotero/storage/5MD2WVP3/1909.html}
}
+@online{wangIntrinsicMagneticTopological2022,
+ title = {Intrinsic {{Magnetic Topological Materials}}},
+ author = {Wang, Yuan and Zhang, Fayuan and Zeng, Meng and Sun, Hongyi and Hao, Zhanyang and Cai, Yongqing and Rong, Hongtao and Zhang, Chengcheng and Liu, Cai and Ma, Xiaoming and Wang, Le and Guo, Shu and Lin, Junhao and Liu, Qihang and Liu, Chang and Chen, Chaoyu},
+ date = {2022-12-18},
+ eprint = {2212.09057},
+ eprinttype = {arxiv},
+ eprintclass = {cond-mat},
+ doi = {10.48550/arXiv.2212.09057},
+ url = {http://arxiv.org/abs/2212.09057},
+ urldate = {2023-06-15},
+ abstract = {Topological states of matter possess bulk electronic structures categorized by topological invariants and edge/surface states due to the bulk-boundary correspondence. Topological materials hold great potential in the development of dissipationless spintronics, information storage, and quantum computation, particularly if combined with magnetic order intrinsically or extrinsically. Here, we review the recent progress in the exploration of intrinsic magnetic topological materials, including but not limited to magnetic topological insulators, magnetic topological metals, and magnetic Weyl semimetals. We pay special attention to their characteristic band features such as the gap of topological surface state, gapped Dirac cone induced by magnetization (either bulk or surface), Weyl nodal point/line, and Fermi arc, as well as the exotic transport responses resulting from such band features. We conclude with a brief envision for experimental explorations of new physics or effects by incorporating other orders in intrinsic magnetic topological materials.},
+ pubstate = {preprint},
+ keywords = {/unread,magnetic materials,magnetic topological materials,topological},
+ file = {/Users/wasmer/Nextcloud/Zotero/Wang et al_2022_Intrinsic Magnetic Topological Materials.pdf;/Users/wasmer/Zotero/storage/MLELX7M9/2212.html}
+}
+
@article{wangLargeScaleDataset2022,
title = {Large Scale Dataset of Real Space Electronic Charge Density of Cubic Inorganic Materials from Density Functional Theory ({{DFT}}) Calculations},
author = {Wang, Fancy Qian and Choudhary, Kamal and Liu, Yu and Hu, Jianjun and Hu, Ming},
@@ -9057,10 +11020,29 @@
urldate = {2023-04-04},
abstract = {We present a symmetry-based exhaustive approach to explore the structural and compositional richness of two-dimensional materials. We use a ``combinatorial engine'' that constructs potential compounds by occupying all possible Wyckoff positions for a certain space group with combinations of chemical elements. These combinations are restricted by imposing charge neutrality and the Pauling test for electronegativities. The structures are then pre-optimized with a specially crafted universal neural-network force-field, before a final step of geometry optimization using density-functional theory is performed. In this way we unveil an unprecedented variety of two-dimensional materials, covering the whole periodic table in more than 30 different stoichiometries of form A\$\_n\$B\$\_m\$ or A\$\_n\$B\$\_m\$C\$\_k\$. Among the found structures we find examples that can be built by decorating nearly all Platonic and Archimedean tesselations as well as their dual Laves or Catalan tilings. We also obtain a rich, and unexpected, polymorphism for some specific compounds. We further accelerate the exploration of the chemical space of two-dimensional materials by employing machine-learning-accelerated prototype search, based on the structural types discovered in the exhaustive search. In total, we obtain around 6500 compounds, not present in previous available databases of 2D materials, with an energy of less than 250\textasciitilde meV/atom above the convex hull of thermodynamic stability.},
pubstate = {preprint},
- keywords = {2D material,2DMatpedia,AML,binary systems,C2DB,CGAT,convex hull,crystal structure,crystal symmetry,M3GNet,materials discovery,materials screening,MC2D,ML,polymorphs,prediction of structure,ternary systems,thermodynamic stability,V2DB,Wyckoff positions},
+ keywords = {\_tablet,2D material,2DMatpedia,AML,binary systems,C2DB,CGAT,convex hull,crystal structure,crystal symmetry,M3GNet,materials discovery,materials screening,MC2D,ML,polymorphs,prediction of structure,ternary systems,thermodynamic stability,V2DB,Wyckoff positions},
file = {/Users/wasmer/Nextcloud/Zotero/Wang et al_2022_Symmetry-based computational search for novel binary and ternary 2D materials.pdf;/Users/wasmer/Zotero/storage/I7GDFM7H/2212.html}
}
+@article{wangTopologicalQuantumMaterials2020,
+ title = {Topological Quantum Materials},
+ author = {Wang, Kang L. and Wu, Yingying and Eckberg, Christopher and Yin, Gen and Pan, Quanjun},
+ date = {2020-05-01},
+ journaltitle = {MRS Bulletin},
+ shortjournal = {MRS Bulletin},
+ volume = {45},
+ number = {5},
+ pages = {373--379},
+ issn = {1938-1425},
+ doi = {10.1557/mrs.2020.122},
+ url = {https://doi.org/10.1557/mrs.2020.122},
+ urldate = {2023-06-14},
+ abstract = {Topological quantum materials are a class of compounds featuring electronic band structures, which are topologically distinct from common metals and insulators. These materials have emerged as exceptionally fertile ground for materials science research. The topologically nontrivial electronic structures of these materials support many interesting properties, ranging from the topologically protected states, manifesting as high mobility and spin-momentum locking, to various quantum Hall effects, axionic physics, and Majorana modes. In this article, we describe different topological matters, including topological insulators, Weyl semimetals, twisted graphene, and related two-dimensional Chern magnetic insulators, as well as their heterostructures. We focus on recent materials discoveries and experimental advancements of topological materials, and their heterostructures. Finally, we conclude with prospects for the discovery of additional topological materials for studying quantum processes, quasiparticles and their composites, as well as exploiting potential applications of these materials.},
+ langid = {english},
+ keywords = {\_tablet},
+ file = {/Users/wasmer/Nextcloud/Zotero/Wang et al_2020_Topological quantum materials.pdf}
+}
+
@article{wangTopologicalStatesCondensed2017,
title = {Topological States of Condensed Matter},
author = {Wang, Jing and Zhang, Shou-Cheng},
@@ -9193,10 +11175,26 @@
urldate = {2022-08-08},
langid = {english},
pagetotal = {99},
- keywords = {_tablet,AiiDA,aiida-kkr,AML,Coulomb matrix,descriptor comparison,impurity embedding,juKKR,KKR,master-thesis,ML,PGI-1/IAS-1,SOAP,thesis},
+ keywords = {\_tablet,AiiDA,aiida-kkr,AML,Coulomb matrix,descriptor comparison,impurity embedding,juKKR,KKR,master-thesis,ML,PGI-1/IAS-1,SOAP,thesis},
file = {/home/johannes/Nextcloud/Zotero/false;/Users/wasmer/Nextcloud/Zotero/Wasmer_2021_Development of a surrogate machine learning model for the acceleration of.pdf;/Users/wasmer/Zotero/storage/AC483X2N/master-thesis.html}
}
+@online{weilerGeneralEquivariantSteerable2021,
+ title = {General {{E}}(2)-{{Equivariant Steerable CNNs}}},
+ author = {Weiler, Maurice and Cesa, Gabriele},
+ date = {2021-04-06},
+ eprint = {1911.08251},
+ eprinttype = {arxiv},
+ eprintclass = {cs, eess},
+ doi = {10.48550/arXiv.1911.08251},
+ url = {http://arxiv.org/abs/1911.08251},
+ urldate = {2023-06-30},
+ abstract = {The big empirical success of group equivariant networks has led in recent years to the sprouting of a great variety of equivariant network architectures. A particular focus has thereby been on rotation and reflection equivariant CNNs for planar images. Here we give a general description of \$E(2)\$-equivariant convolutions in the framework of Steerable CNNs. The theory of Steerable CNNs thereby yields constraints on the convolution kernels which depend on group representations describing the transformation laws of feature spaces. We show that these constraints for arbitrary group representations can be reduced to constraints under irreducible representations. A general solution of the kernel space constraint is given for arbitrary representations of the Euclidean group \$E(2)\$ and its subgroups. We implement a wide range of previously proposed and entirely new equivariant network architectures and extensively compare their performances. \$E(2)\$-steerable convolutions are further shown to yield remarkable gains on CIFAR-10, CIFAR-100 and STL-10 when used as a drop-in replacement for non-equivariant convolutions.},
+ pubstate = {preprint},
+ keywords = {/unread,2D,CNN,computer vision,E(2),equivariant,General ML,library,ML,steerable CNN,with-code},
+ file = {/Users/wasmer/Nextcloud/Zotero/Weiler_Cesa_2021_General $E(2)$-Equivariant Steerable CNNs.pdf;/Users/wasmer/Zotero/storage/49VD5PUN/1911.html}
+}
+
@article{weinertSolutionPoissonEquation1981,
title = {Solution of {{Poisson}}’s Equation: {{Beyond Ewald}}‐type Methods},
shorttitle = {Solution of {{Poisson}}’s Equation},
@@ -9227,7 +11225,7 @@
abstract = {Chemists can be skeptical in using deep learning (DL) in decision making, due to the lack of interpretability in "black-box" models. Explainable artificial intelligence (XAI) is a branch of AI which addresses this drawback by providing tools to interpret DL models and their predictions. We review the principles of XAI in the domain of chemistry and emerging methods for creating and evaluating explanations. Then we focus methods developed by our group and their application to predicting solubility, blood-brain barrier permeability, and the scent of molecules. We show that XAI methods like chemical counterfactuals and descriptor explanations can both explain DL predictions and give insight into structure-property relationships. Finally, we discuss how a two step process of highly accurate black-box modeling and then creating explanations gives both highly accurate predictions and clear structure-property relationships.},
langid = {english},
pubstate = {preprint},
- keywords = {_tablet,counterfactual explanation,Deep learning,GNN,molecules,XAI},
+ keywords = {\_tablet,counterfactual explanation,Deep learning,GNN,molecules,XAI},
file = {/Users/wasmer/Nextcloud/Zotero/Wellawatte et al_2022_A Perspective on Explanations of Molecular Prediction Models.pdf}
}
@@ -9264,6 +11262,26 @@
file = {/Users/wasmer/Nextcloud/Zotero/Westermayr et al_2021_Perspective on integrating machine learning into computational chemistry and.pdf;/Users/wasmer/Nextcloud/Zotero/Westermayr et al_2021_Perspective on integrating machine learning into computational chemistry and2.pdf;/Users/wasmer/Zotero/storage/FHJLNQAU/2102.html}
}
+@article{whiteAssessmentChemistryKnowledge2023,
+ title = {Assessment of Chemistry Knowledge in Large Language Models That Generate Code},
+ author = {White, Andrew D. and Hocky, Glen M. and Gandhi, Heta A. and Ansari, Mehrad and Cox, Sam and Wellawatte, Geemi P. and Sasmal, Subarna and Yang, Ziyue and Liu, Kangxin and Singh, Yuvraj and Ccoa, Willmor J. Peña},
+ date = {2023-04-11},
+ journaltitle = {Digital Discovery},
+ shortjournal = {Digital Discovery},
+ volume = {2},
+ number = {2},
+ pages = {368--376},
+ publisher = {{RSC}},
+ issn = {2635-098X},
+ doi = {10.1039/D2DD00087C},
+ url = {https://pubs.rsc.org/en/content/articlelanding/2023/dd/d2dd00087c},
+ urldate = {2023-05-20},
+ abstract = {In this work, we investigate the question: do code-generating large language models know chemistry? Our results indicate, mostly yes. To evaluate this, we introduce an expandable framework for evaluating chemistry knowledge in these models, through prompting models to solve chemistry problems posed as coding tasks. To do so, we produce a benchmark set of problems, and evaluate these models based on correctness of code by automated testing and evaluation by experts. We find that recent LLMs are able to write correct code across a variety of topics in chemistry and their accuracy can be increased by 30 percentage points via prompt engineering strategies, like putting copyright notices at the top of files. Our dataset and evaluation tools are open source which can be contributed to or built upon by future researchers, and will serve as a community resource for evaluating the performance of new models as they emerge. We also describe some good practices for employing LLMs in chemistry. The general success of these models demonstrates that their impact on chemistry teaching and research is poised to be enormous.},
+ langid = {english},
+ keywords = {AML,best practices,code generation,Computational chemistry,Database,education,GPT,GPT-3,LLM,ML,prompt engineering,simulation},
+ file = {/Users/wasmer/Nextcloud/Zotero/White et al_2023_Assessment of chemistry knowledge in large language models that generate code.pdf}
+}
+
@article{whiteDeepLearningMolecules2021,
title = {Deep {{Learning}} for {{Molecules}} and {{Materials}}},
author = {White, Andrew D.},
@@ -9279,10 +11297,28 @@
abstract = {Deep learning is becoming a standard tool in chemistry and materials science. Although there are learning materials available for deep learning, none cover the applications in chemistry and materials science or the peculiarities of working with molecules. The textbook described here provides a systematic and applied introduction to the latest research in deep learning in chemistry and materials science. It covers the math fundamentals, the requisite machine learning, the common neural network architectures used today, and the details necessary to be a practitioner of deep learning. The textbook is a living document and will be updated as the rapidly changing deep learning field evolves.},
issue = {1},
langid = {english},
- keywords = {_tablet,book,GNN,ML-DFT,ML-ESM,MLP,MPNN,tutorial},
+ keywords = {\_tablet,book,GNN,ML-DFT,ML-ESM,MLP,MPNN,tutorial},
file = {/Users/wasmer/Nextcloud/Zotero/White_2021_Deep Learning for Molecules and Materials.pdf}
}
+@article{whiteFutureChemistryLanguage2023,
+ title = {The Future of Chemistry Is Language},
+ author = {White, Andrew D.},
+ date = {2023-05-19},
+ journaltitle = {Nature Reviews Chemistry},
+ shortjournal = {Nat Rev Chem},
+ pages = {1--2},
+ publisher = {{Nature Publishing Group}},
+ issn = {2397-3358},
+ doi = {10.1038/s41570-023-00502-0},
+ url = {https://www.nature.com/articles/s41570-023-00502-0},
+ urldate = {2023-05-20},
+ abstract = {Large language models such as GPT-4 have been approaching human-level ability across many expert domains. GPT-4 can accomplish complex tasks in chemistry purely from English instructions, which may transform the future of chemistry.},
+ langid = {english},
+ keywords = {AML,cheminformatics,chemistry,Deep learning,experiment planning,GPT,GPT-4,I/O,literature analysis,LLM,ML,nlp,pretrained models,property prediction,RSE,SMILES,software engineering,structure prediction,XAI},
+ file = {/Users/wasmer/Nextcloud/Zotero/White_2023_The future of chemistry is language.pdf}
+}
+
@article{wiederCompactReviewMolecular2020,
title = {A Compact Review of Molecular Property Prediction with Graph Neural Networks},
author = {Wieder, Oliver and Kohlbacher, Stefan and Kuenemann, Mélaine and Garon, Arthur and Ducrot, Pierre and Seidel, Thomas and Langer, Thierry},
@@ -9297,7 +11333,7 @@
urldate = {2022-09-27},
abstract = {As graph neural networks are becoming more and more powerful and useful in the field of drug discovery, many pharmaceutical companies are getting interested in utilizing these methods for their own in-house frameworks. This is especially compelling for tasks such as the prediction of molecular properties which is often one of the most crucial tasks in computer-aided drug discovery workflows. The immense hype surrounding these kinds of algorithms has led to the development of many different types of promising architectures and in this review we try to structure this highly dynamic field of AI-research by collecting and classifying 80 GNNs that have been used to predict more than 20 molecular properties using 48 different datasets.},
langid = {english},
- keywords = {_tablet,chemistry,GCN,GNN,molecules,review},
+ keywords = {\_tablet,chemistry,GCN,GNN,molecules,review},
file = {/Users/wasmer/Nextcloud/Zotero/Wieder et al_2020_A compact review of molecular property prediction with graph neural networks.pdf;/Users/wasmer/Zotero/storage/KHCYV2ZB/S1740674920300305.html}
}
@@ -9405,7 +11441,7 @@
urldate = {2023-04-04},
abstract = {The impact of magnetism on predicted atomic short-range order in Ni-based high-entropy alloys is studied using a first-principles, all-electron, Landau-type linear response theory, coupled with lattice-based atomistic modelling. We perform two sets of linear-response calculations: one in which the paramagnetic state is modelled within the disordered local moment picture, and one in which systems are modelled in a magnetically ordered state. We show that the treatment of magnetism can have significant impact both on the predicted temperature of atomic ordering and also the nature of atomic order itself. In CrCoNi, we find that the nature of atomic order changes from being \$L1\_2\$-like when modelled in the paramagnetic state to MoPt\$\_2\$-like when modelled assuming the system has magnetically ordered. In CrFeCoNi, atomic correlations between Fe and the other elements present are dramatically strengthened when we switch from treating the system as magnetically disordered to magnetically ordered. Our results show it is necessary to consider the magnetic state when modelling multicomponent alloys containing mid- to late-\$3d\$ elements. Further, we suggest that there may be high-entropy alloy compositions containing \$3d\$ transition metals that will exhibit specific atomic short-range order when thermally treated in an applied magnetic field.},
pubstate = {preprint},
- keywords = {/unread,CPA,DFT,high-entropy alloys,KKR,n-ary alloys,transition metals},
+ keywords = {\_tablet,/unread,CPA,DFT,high-entropy alloys,KKR,n-ary alloys,transition metals},
file = {/Users/wasmer/Nextcloud/Zotero/Woodgate et al_2023_Interplay between magnetism and short-range order in Ni-based high-entropy.pdf;/Users/wasmer/Zotero/storage/RAQ822ZS/2303.html}
}
@@ -9422,6 +11458,20 @@
file = {/Users/wasmer/Zotero/storage/Y7SURWT5/quantum-complexity-tamed-by-machine-learning-20220207.html}
}
+@software{wortmannFLEUR2023,
+ title = {{{FLEUR}}},
+ author = {Wortmann, Daniel and Michalicek, Gregor and Baadji, Nadjib and Betzinger, Markus and Bihlmayer, Gustav and Bröder, Jens and Burnus, Tobias and Enkovaara, Jussi and Freimuth, Frank and Friedrich, Christoph and Gerhorst, Christian-Roman and Granberg Cauchi, Sabastian and Grytsiuk, Uliana and Hanke, Andrea and Hanke, Jan-Philipp and Heide, Marcus and Heinze, Stefan and Hilgers, Robin and Janssen, Henning and Klüppelberg, Daniel Aaaron and Kovacik, Roman and Kurz, Philipp and Lezaic, Marjana and Madsen, Georg K. H. and Mokrousov, Yuriy and Neukirchen, Alexander and Redies, Matthias and Rost, Stefan and Schlipf, Martin and Schindlmayr, Arno and Winkelmann, Miriam and Blügel, Stefan},
+ date = {2023-05-03},
+ doi = {10.5281/zenodo.7891361},
+ url = {https://zenodo.org/record/7891361},
+ urldate = {2023-05-08},
+ abstract = {FLEUR is an all-electron DFT code based on the full-potential linearized augmented plane-wave method (FLAPW). It is mainly developed at the Forschungsentrum Jülich, Germany and available for the materials research community.},
+ organization = {{Forschungsentrum Jülich}},
+ version = {MaX-R6.2},
+ keywords = {All-electron,Density functional theory,DFT,FLAPW,FLEUR},
+ file = {/Users/wasmer/Zotero/storage/KYZY7YXB/7891361.html}
+}
+
@article{wurgerExploringStructurepropertyRelationships2021,
title = {Exploring Structure-Property Relationships in Magnesium Dissolution Modulators},
author = {Würger, Tim and Mei, Di and Vaghefinazari, Bahram and Winkler, David A. and Lamaka, Sviatlana V. and Zheludkevich, Mikhail L. and Meißner, Robert H. and Feiler, Christian},
@@ -9458,10 +11508,70 @@
url = {https://link.aps.org/doi/10.1103/PhysRevLett.120.145301},
urldate = {2022-09-27},
abstract = {The use of machine learning methods for accelerating the design of crystalline materials usually requires manually constructed feature vectors or complex transformation of atom coordinates to input the crystal structure, which either constrains the model to certain crystal types or makes it difficult to provide chemical insights. Here, we develop a crystal graph convolutional neural networks framework to directly learn material properties from the connection of atoms in the crystal, providing a universal and interpretable representation of crystalline materials. Our method provides a highly accurate prediction of density functional theory calculated properties for eight different properties of crystals with various structure types and compositions after being trained with 104 data points. Further, our framework is interpretable because one can extract the contributions from local chemical environments to global properties. Using an example of perovskites, we show how this information can be utilized to discover empirical rules for materials design.},
- keywords = {_tablet,CGCNN,GCN,GNN,library,solids,with-code},
+ keywords = {\_tablet,CGCNN,GCN,GNN,library,solids,with-code},
file = {/Users/wasmer/Nextcloud/Zotero/Xie_Grossman_2018_Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable.pdf}
}
+@article{xieFunctionalFormSuperconducting2019,
+ title = {Functional Form of the Superconducting Critical Temperature from Machine Learning},
+ author = {Xie, S. R. and Stewart, G. R. and Hamlin, J. J. and Hirschfeld, P. J. and Hennig, R. G.},
+ date = {2019-11-18},
+ journaltitle = {Physical Review B},
+ shortjournal = {Phys. Rev. B},
+ volume = {100},
+ number = {17},
+ pages = {174513},
+ publisher = {{American Physical Society}},
+ doi = {10.1103/PhysRevB.100.174513},
+ url = {https://link.aps.org/doi/10.1103/PhysRevB.100.174513},
+ urldate = {2023-05-06},
+ abstract = {Predicting the critical temperature Tc of new superconductors is a notoriously difficult task, even for electron-phonon paired superconductors, for which the theory is relatively well understood. Early attempts to obtain a simple Tc formula consistent with strong-coupling theory, by McMillan and by Allen and Dynes, led to closed-form approximate relations between Tc and various measures of the phonon spectrum and the electron-phonon interaction appearing in Eliashberg theory. Here we propose that these approaches can be improved with the use of machine-learning algorithms. As an initial test, we train a model for identifying low-dimensional descriptors using the Tc{$<$}10 K dataset by Allen and Dynes, and show that a simple analytical expression thus obtained improves upon the Allen-Dynes fit. Furthermore, the prediction for the recently discovered high-Tc material H3S at high pressure is quite reasonable. Interestingly, Tc's for more recently discovered superconducting systems with a more two-dimensional electron-phonon coupling, which do not follow Allen and Dynes's expression, also do not follow our analytic expression. Thus, this machine-learning approach appears to be a powerful method for highlighting the need for a new descriptor beyond those used by Allen and Dynes to describe their set of isotropic electron-phonon coupled superconductors. We argue that this machine-learning method, and its implied need for a descriptor characterizing Fermi-surface properties, represents a promising approach to superconductor materials discovery which may eventually replace the serendipitous discovery paradigm begun by Kamerlingh Onnes.},
+ keywords = {/unread,Allen-Dynes equation,AML,Curie temperature,hydrides,ML,prediction of Curie temperature,SISSO,superconductor,symbolic regression},
+ file = {/Users/wasmer/Nextcloud/Zotero/Xie et al_2019_Functional form of the superconducting critical temperature from machine.pdf}
+}
+
+@article{xieHighthroughputSuperconductorDiscovery2022,
+ title = {Towards High-Throughput Superconductor Discovery via Machine Learning},
+ author = {Xie, Stephen R. and Quan, Y. and Hire, Ajinkya and Fanfarillo, Laura and Stewart, G. R. and Hamlin, J. J. and Hennig, R. G. and Hirschfeld, P. J.},
+ date = {2022-05-04},
+ journaltitle = {Journal of Physics: Condensed Matter},
+ shortjournal = {J. Phys.: Condens. Matter},
+ volume = {34},
+ number = {18},
+ eprint = {2104.11150},
+ eprinttype = {arxiv},
+ eprintclass = {cond-mat},
+ pages = {183002},
+ issn = {0953-8984, 1361-648X},
+ doi = {10.1088/1361-648X/ac2864},
+ url = {http://arxiv.org/abs/2104.11150},
+ urldate = {2023-05-06},
+ abstract = {Even though superconductivity has been studied intensively for more than a century, the vast majority of superconductivity research today is carried out in nearly the same manner as decades ago. That is, each study tends to focus on only a single material or small subset of materials, and discoveries are made more or less serendipitously. Recent increases in computing power, novel machine learning algorithms, and improved experimental capabilities offer new opportunities to revolutionize superconductor discovery. These will enable the rapid prediction of structures and properties of novel materials in an automated, high-throughput fashion and the efficient experimental testing of these predictions. Here, we review efforts to use machine learning to attain this goal.},
+ keywords = {/unread,Allen-Dynes equation,AML,autoencoder,Eliashberg theory,hydrides,materials discovery,materials screening,ML,superconductor,symbolic regression},
+ file = {/Users/wasmer/Nextcloud/Zotero/Xie et al_2022_Towards high-throughput superconductor discovery via machine learning.pdf;/Users/wasmer/Zotero/storage/IVC6AHH2/2104.html}
+}
+
+@article{xieMachineLearningSuperconducting2022,
+ title = {Machine Learning of Superconducting Critical Temperature from {{Eliashberg}} Theory},
+ author = {Xie, S. R. and Quan, Y. and Hire, A. C. and Deng, B. and DeStefano, J. M. and Salinas, I. and Shah, U. S. and Fanfarillo, L. and Lim, J. and Kim, J. and Stewart, G. R. and Hamlin, J. J. and Hirschfeld, P. J. and Hennig, R. G.},
+ date = {2022-01-25},
+ journaltitle = {npj Computational Materials},
+ shortjournal = {npj Comput Mater},
+ volume = {8},
+ number = {1},
+ pages = {1--8},
+ publisher = {{Nature Publishing Group}},
+ issn = {2057-3960},
+ doi = {10.1038/s41524-021-00666-7},
+ url = {https://www.nature.com/articles/s41524-021-00666-7},
+ urldate = {2023-05-06},
+ abstract = {The Eliashberg theory of superconductivity accounts for the fundamental physics of conventional superconductors, including the retardation of the interaction and the Coulomb pseudopotential, to predict the critical temperature Tc. McMillan, Allen, and Dynes derived approximate closed-form expressions for the critical temperature within this theory, which depends on the electron–phonon spectral function α2F(ω). Here we show that modern machine-learning techniques can substantially improve these formulae, accounting for more general shapes of the α2F function. Using symbolic regression and the SISSO framework, together with a database of artificially generated α2F functions and numerical solutions of the Eliashberg equations, we derive a formula for Tc that performs as well as Allen–Dynes for low-Tc superconductors and substantially better for higher-Tc ones. This corrects the systematic underestimation of Tc while reproducing the physical constraints originally outlined by Allen and Dynes. This equation should replace the Allen–Dynes formula for the prediction of higher-temperature superconductors.},
+ issue = {1},
+ langid = {english},
+ keywords = {/unread,AML,conventional superconductor,Curie temperature,Eliashberg theory,ML,prediction of Curie temperature,SISSO,superconductor,symbolic regression,with-code},
+ file = {/Users/wasmer/Nextcloud/Zotero/Xie et al_2022_Machine learning of superconducting critical temperature from Eliashberg theory.pdf}
+}
+
@unpublished{xieUltrafastInterpretableMachinelearning2021,
title = {Ultra-Fast Interpretable Machine-Learning Potentials},
author = {Xie, Stephen R. and Rupp, Matthias and Hennig, Richard G.},
@@ -9476,6 +11586,22 @@
file = {/Users/wasmer/Nextcloud/Zotero/Xie et al_2021_Ultra-fast interpretable machine-learning potentials.pdf;/Users/wasmer/Zotero/storage/8585X9YA/2110.html}
}
+@online{xieUltrafastInterpretableMachinelearning2021a,
+ title = {Ultra-Fast Interpretable Machine-Learning Potentials},
+ author = {Xie, Stephen R. and Rupp, Matthias and Hennig, Richard G.},
+ date = {2021-10-01},
+ eprint = {2110.00624},
+ eprinttype = {arxiv},
+ eprintclass = {cond-mat, physics:physics},
+ doi = {10.48550/arXiv.2110.00624},
+ url = {http://arxiv.org/abs/2110.00624},
+ urldate = {2023-05-06},
+ abstract = {All-atom dynamics simulations are an indispensable quantitative tool in physics, chemistry, and materials science, but large systems and long simulation times remain challenging due to the trade-off between computational efficiency and predictive accuracy. To address this challenge, we combine effective two- and three-body potentials in a cubic B-spline basis with regularized linear regression to obtain machine-learning potentials that are physically interpretable, sufficiently accurate for applications, as fast as the fastest traditional empirical potentials, and two to four orders of magnitude faster than state-of-the-art machine-learning potentials. For data from empirical potentials, we demonstrate exact retrieval of the potential. For data from density functional theory, the predicted energies, forces, and derived properties, including phonon spectra, elastic constants, and melting points, closely match those of the reference method. The introduced potentials might contribute towards accurate all-atom dynamics simulations of large atomistic systems over long time scales.},
+ pubstate = {preprint},
+ keywords = {/unread,AML,B-splines,body-order,descriptors,GAP,library,linear regression,ML,MLP,MLP comparison,original publication,qSNAP,SNAP,UF3},
+ file = {/Users/wasmer/Nextcloud/Zotero/false;/Users/wasmer/Zotero/storage/B9DGEUPF/2110.html}
+}
+
@article{xuSurveyMultiOutputLearning2020,
title = {Survey on {{Multi-Output Learning}}},
author = {Xu, Donna and Shi, Yaxin and Tsang, Ivor W. and Ong, Yew-Soon and Gong, Chen and Shen, Xiaobo},
@@ -9595,10 +11721,23 @@
url = {https://aip.scitation.org/doi/10.1063/5.0049665},
urldate = {2021-06-24},
abstract = {Geometry optimization is an important part of both computational materials and surface science because it is the path to finding ground state atomic structures and reaction pathways. These properties are used in the estimation of thermodynamic and kinetic properties of molecular and crystal structures. This process is slow at the quantum level of theory because it involves an iterative calculation of forces using quantum chemical codes such as density functional theory (DFT), which are computationally expensive and which limit the speed of the optimization algorithms. It would be highly advantageous to accelerate this process because then one could do either the same amount of work in less time or more work in the same time. In this work, we provide a neural network (NN) ensemble based active learning method to accelerate the local geometry optimization for multiple configurations simultaneously. We illustrate the acceleration on several case studies including bare metal surfaces, surfaces with adsorbates, and nudged elastic band for two reactions. In all cases, the accelerated method requires fewer DFT calculations than the standard method. In addition, we provide an Atomic Simulation Environment (ASE)-optimizer Python package to make the usage of the NN ensemble active learning for geometry optimization easier.},
- keywords = {ACSF,active learning,BPNN,DFT,GPR,ML,SingleNN,structure relaxation,surrogate model,to_read},
+ keywords = {ACSF,active learning,BPNN,DFT,GPR,ML,SingleNN,structure relaxation,surrogate model,to\_read},
file = {/home/johannes/Nextcloud/Zotero/false;/Users/wasmer/Zotero/storage/2L5JFJN8/5.html}
}
+@online{yuSpinDependentGraphNeural2023,
+ title = {Spin-{{Dependent Graph Neural Network Potential}} for {{Magnetic Materials}}},
+ author = {Yu, Hongyu and Zhong, Yang and Hong, Liangliang and Xu, Changsong and Ren, Wei and Gong, Xingao and Xiang, Hongjun},
+ date = {2023-05-11},
+ doi = {10.21203/rs.3.rs-2839528/v1},
+ url = {https://www.researchsquare.com},
+ urldate = {2023-06-12},
+ abstract = {The development of machine learning interatomic potentials has immensely contributed to the accuracy of simulations of molecules and crystals. However, creating interatomic potentials for magnetic systems that account for both magnetic moments and structural degrees of freedom remains a challe...},
+ langid = {english},
+ keywords = {\_tablet,Allegro,AML,collinear,DimeNet,equivariant,GNN,HDNNP,heat transport,Heisenberg model,Jij,LAMMPS,LAMMPS SPIN,magnetism,MD,mHDNNP,ML,MLP,mMTP,MTP,multiferroic,non-collinear,original publication,PES,prediction of Jij,prediction of magnetic ground state,spin dynamics,Spin-Allegro,spin-dependent,Spin-Dimenet,spin-lattice coupling,SpinGNN,with-code},
+ file = {/Users/wasmer/Nextcloud/Zotero/Yu et al_2023_Spin-Dependent Graph Neural Network Potential for Magnetic Materials.pdf}
+}
+
@online{zachglickDoesItFeel2021,
type = {Tweet},
title = {Does It Feel like Everyone in \#compchem Is Doing Machine Learning Now? {{I}} Thought so after \#{{ACSSpring2021}}, and Decided to Look at How Frequently Certain Phrases Appeared in {{COMP}} Division Abstracts over the Years: {{https://t.co/F4awnzVebs}}},
@@ -9662,17 +11801,17 @@
@report{zellerCorrelatedElectronsModels2012,
title = {Correlated Electrons: From Models to Materials},
author = {Zeller, Rudolf},
+ editorb = {Pavarini, Eva and Anders, Frithjof and Koch, Erik and Jarrell, Mark},
+ editorbtype = {redactor},
date = {2012},
number = {PreJuSER-136393},
institution = {{Forschungszentrum Jülich GmbH Zenralbibliothek, Verlag}},
url = {https://juser.fz-juelich.de/record/136393/},
urldate = {2022-06-28},
abstract = {Density-functional theory (DFT) is considered the Standard Model of solid-state physics. The state-of-the-art approximations to DFT, the local-density approximation (LDA) or its simple extensions, fail, however, even qualitatively, for strongly-correlated systems. When correlations are strong, electrons become entangled and novel properties emerge. Mott-transitions, Kondo- and heavy-fermion behavior, non-conventional superconductivity and orbital-order are just some examples of this emergent behavior. The realistic description of emergent properties is one of the grand-challenges of modern condensed-matter physics. To understand this physics beyond the Standard Model, nonperturbative many-body techniques are essential. Still, DFT-based methods are needed to devise materials-specific Hamiltonians for strong correlations. Mastering these novel techniques requires a vast background, ranging from DFT to model building and many-body physics. The aim of this school is to introduce advanced graduate students and up to the modern methods for modeling emergent properties of correlated electrons and to explore the relation of electron correlations with quantum entanglement and concepts from quantum information. A school of this size and scope requires support and help from many sources. We are very grateful for all the financial and practical support we have received. The Institute for Advanced Simulation and the German Research School for Simulation Sciences at the Forschungszentrum Jülich provided the funding and were vital for the organization of the school and the production of this book. The DFG Forschergruppe FOR1346 offered travel grants for students and the Institute for Complex Adaptive Matter (ICAM) travel support for international speakers and participants. The nature of a school makes it desirable to have the lecture-notes available already during the lectures. In this way the participants get the chance to work through the lectures thoroughly while they are given. We are therefore extremely grateful to the lecturers that, despite a tight schedule, provided their manuscripts in time for the production of this book. We are confident that the lecture notes collected here will not only serve the participants of the school but will also be useful for other students entering the exciting field of strongly correlated materials. We thank Mrs. H. Lexis of the Forschungszentrum Jülich Verlag and Mr. D. Laufenberg of the Graphische Betriebe for providing their expert support in producing the present volume on a tight schedule and for making even seemingly impossible requests possible. We heartily thank our students and postdocs that helped in proofreading the manuscripts, often on short notice: Carmine Autieri, Fabio Baruffa, Michael Baumgärtel, Monica Bugeanu, Andreas Flesch, Evgeny Gorelov, Amin Kiani Sheikhabadi, Joaquin Miranda, German Ulm, and Guoren Zhang. Finally, our special thanks go to Dipl.-Ing. R. Hölzle for his invaluable advice on all questions concerning the organization of such a school and to Mrs. L. Snyders and Mrs. E. George for expertly handling all practical issues. Pavarini, Eva; Koch, Erik; Anders, Frithjof; Jarrell, Mark (Eds. )},
- editorb = {Pavarini, Eva and Anders, Frithjof and Koch, Erik and Jarrell, Mark},
- editorbtype = {redactor},
isbn = {9783893367962},
langid = {english},
- keywords = {_tablet,CPA,KKR,PGI-1/IAS-1,VCA},
+ keywords = {\_tablet,CPA,KKR,PGI-1/IAS-1,VCA},
file = {/Users/wasmer/Nextcloud/Zotero/Zeller_2012_Correlated electrons.pdf;/Users/wasmer/Zotero/storage/BKBRXSWN/136393.html}
}
@@ -9691,7 +11830,7 @@
url = {https://aip.scitation.org/doi/10.1063/5.0052961},
urldate = {2022-05-11},
abstract = {We probe the accuracy of linear ridge regression employing a three-body local density representation derived from the atomic cluster expansion. We benchmark the accuracy of this framework in the prediction of formation energies and atomic forces in molecules and solids. We find that such a simple regression framework performs on par with state-of-the-art machine learning methods which are, in most cases, more complex and more computationally demanding. Subsequently, we look for ways to sparsify the descriptor and further improve the computational efficiency of the method. To this aim, we use both principal component analysis and least absolute shrinkage operator regression for energy fitting on six single-element datasets. Both methods highlight the possibility of constructing a descriptor that is four times smaller than the original with a similar or even improved accuracy. Furthermore, we find that the reduced descriptors share a sizable fraction of their features across the six independent datasets, hinting at the possibility of designing material-agnostic, optimally compressed, and accurate descriptors.},
- keywords = {_tablet,ACE,descriptor dimred,descriptors,dimensionality reduction},
+ keywords = {\_tablet,ACE,descriptor dimred,descriptors,dimensionality reduction},
file = {/Users/wasmer/Nextcloud/Zotero/Zeni et al_2021_Compact atomic descriptors enable accurate predictions via linear models.pdf}
}
@@ -9710,7 +11849,7 @@
urldate = {2022-07-08},
abstract = {The recently developed Deep Potential [Phys. Rev. Lett. 120 (2018) 143001 [27]] is a powerful method to represent general inter-atomic potentials using deep neural networks. The success of Deep Potential rests on the proper treatment of locality and symmetry properties of each component of the network. In this paper, we leverage its network structure to effectively represent the mapping from the atomic configuration to the electron density in Kohn-Sham density function theory (KS-DFT). By directly targeting at the self-consistent electron density, we demonstrate that the adapted network architecture, called the Deep Density, can effectively represent the self-consistent electron density as the linear combination of contributions from many local clusters. The network is constructed to satisfy the translation, rotation, and permutation symmetries, and is designed to be transferable to different system sizes. We demonstrate that using a relatively small number of training snapshots, with each snapshot containing a modest amount of data-points, Deep Density achieves excellent performance for one-dimensional insulating and metallic systems, as well as systems with mixed insulating and metallic characters. We also demonstrate its performance for real three-dimensional systems, including small organic molecules, as well as extended systems such as water (up to 512 molecules) and aluminum (up to 256 atoms).},
langid = {english},
- keywords = {_tablet,DFT,ML,ML-DFT,ML-ESM,prediction of electron density},
+ keywords = {\_tablet,DFT,ML,ML-DFT,ML-ESM,prediction of electron density},
file = {/Users/wasmer/Nextcloud/Zotero/Zepeda-Núñez et al_2021_Deep Density.pdf;/Users/wasmer/Zotero/storage/TJJ4NCEI/S0021999121004186.html}
}
@@ -9796,7 +11935,7 @@
urldate = {2023-02-23},
abstract = {Determining thermal and physical quantities across a broad temperature domain, especially up to the ultra-high temperature region, is a formidable theoretical and experimental challenge. At the same time it is essential for understanding the performance of ultra-high temperature ceramic (UHTC) materials. Here we present the development of a machine-learning force field for ZrB2, one of the primary members of the UHTC family with a complex bonding structure. The force field exhibits chemistry accuracy for both energies and forces and can reproduce structural, elastic and phonon properties, including thermal expansion and thermal transport. A thorough comparison with available empirical potentials shows that our force field outperforms the competitors. Most importantly, its effectiveness is extended from room temperature to the ultra-high temperature region (up to \textasciitilde{} 2,500 K), where measurements are very difficult, costly and some time impossible. Our work demonstrates that machine-learning force fields can be used for simulations of materials in a harsh environment, where no experimental tools are available, but crucial for a number of engineering applications, such as in aerospace, aviation and nuclear.},
pubstate = {preprint},
- keywords = {_tablet,/unread,Condensed Matter - Materials Science,Quantum Physics},
+ keywords = {\_tablet,/unread,Condensed Matter - Materials Science,Quantum Physics},
file = {/Users/wasmer/Nextcloud/Zotero/Zhang et al_2019_Pushing the limits of atomistic simulations towards ultra-high temperature.pdf;/Users/wasmer/Zotero/storage/IIZUJ8Y9/1911.html}
}
@@ -9838,6 +11977,24 @@
file = {/Users/wasmer/Nextcloud/Zotero/Zhang_Ling_2018_A strategy to apply machine learning to small datasets in materials science.pdf;/Users/wasmer/Zotero/storage/PEGZREYC/s41524-018-0081-z.html}
}
+@article{zhangTopologicalInsulatorsPerspective2013,
+ title = {Topological Insulators from the Perspective of First-Principles Calculations},
+ author = {Zhang, Haijun and Zhang, Shou-Cheng},
+ date = {2013},
+ journaltitle = {physica status solidi (RRL) – Rapid Research Letters},
+ volume = {7},
+ number = {1-2},
+ pages = {72--81},
+ issn = {1862-6270},
+ doi = {10.1002/pssr.201206414},
+ url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/pssr.201206414},
+ urldate = {2023-06-23},
+ abstract = {Topological insulators are new quantum states with helical gapless edge or surface states inside the bulk band gap. These topological surface states are robust against weak time-reversal invariant perturbations without closing the bulk band gap, such as lattice distortions and non-magnetic impurities. Recently a variety of topological insulators have been predicted by theories, and observed by experiments. First-principles calculations have been widely used to predict topological insulators with great success. In this review, we summarize the current progress in this field from the perspective of first-principles calculations. First of all, the basic concepts of topological insulators and the frequently-used techniques within first-principles calculations are briefly introduced. Secondly, we summarize general methodologies to search for new topological insulators. In the last part, based on the band inversion picture first introduced in the context of HgTe, we classify topological insulators into three types with s–p, p–p and d–f, and discuss some representative examples for each type. Surface states of topological insulator Bi2Se3 consist of a single Dirac cone, as obtained from first-principles calculations. (© 2013 WILEY-VCH Verlag GmbH \& Co. KGaA, Weinheim)},
+ langid = {english},
+ keywords = {\_tablet,DFT,DMFT,Hall effect,Hall QAHE,LDA,LDA+DMFT,LDA+U,review,SOC,topological insulator},
+ file = {/Users/wasmer/Nextcloud/Zotero/Zhang_Zhang_2013_Topological insulators from the perspective of first-principles calculations.pdf;/Users/wasmer/Zotero/storage/ZU36AAB4/pssr.html}
+}
+
@article{zhaoQuantumOscillationsIrondoped2019,
title = {Quantum Oscillations in Iron-Doped Single Crystals of the Topological Insulator \$\textbackslash mathrm\{\vphantom\}{{S}}\vphantom\{\}\{\textbackslash mathrm\{b\}\}\_\{2\}\textbackslash mathrm\{\vphantom\}{{T}}\vphantom\{\}\{\textbackslash mathrm\{e\}\}\_\{3\}\$},
author = {Zhao, Weiyao and Cortie, David and Chen, Lei and Li, Zhi and Yue, Zengji and Wang, Xiaolin},
@@ -9855,6 +12012,22 @@
file = {/Users/wasmer/Nextcloud/Zotero/Zhao et al_2019_Quantum oscillations in iron-doped single crystals of the topological insulator.pdf;/Users/wasmer/Zotero/storage/GGTED6FM/Zhao et al. - 2019 - Quantum oscillations in iron-doped single crystals.pdf;/Users/wasmer/Zotero/storage/8D5JL2DQ/PhysRevB.99.html}
}
+@online{zhongAcceleratingElectronicstructureCalculation2023,
+ title = {Accelerating the Electronic-Structure Calculation of Magnetic Systems by Equivariant Neural Networks},
+ author = {Zhong, Yang and Zhang, Binhua and Yu, Hongyu and Gong, Xingao and Xiang, Hongjun},
+ date = {2023-06-02},
+ eprint = {2306.01558},
+ eprinttype = {arxiv},
+ eprintclass = {cond-mat, physics:physics},
+ doi = {10.48550/arXiv.2306.01558},
+ url = {http://arxiv.org/abs/2306.01558},
+ urldate = {2023-06-12},
+ abstract = {Complex spin-spin interactions in magnets can often lead to magnetic superlattices with complex local magnetic arrangements, and many of the magnetic superlattices have been found to possess non-trivial topological electronic properties. Due to the huge size and complex magnetic moment arrangement of the magnetic superlattices, it is a great challenge to perform a direct DFT calculation on them. In this work, an equivariant deep learning framework is designed to accelerate the electronic calculation of magnetic systems by exploiting both the equivariant constraints of the magnetic Hamiltonian matrix and the physical rules of spin-spin interactions. This framework can bypass the costly self-consistent iterations and build a direct mapping from a magnetic configuration to the ab initio Hamiltonian matrix. After training on the magnets with random magnetic configurations, our model achieved high accuracy on the test structures outside the training set, such as spin spiral and non-collinear antiferromagnetic configurations. The trained model is also used to predict the energy bands of a skyrmion configuration of NiBrI containing thousands of atoms, showing the high efficiency of our model on large magnetic superlattices.},
+ pubstate = {preprint},
+ keywords = {\_tablet,2D material,AFM,AML,DFT,Dzyaloshinskii–Moriya interaction,E(3),equivariant,GNN,Hall effect,Heisenberg model,iron,Jij,magnetic Hamiltonian,magnetic supperlattice,magnetism,ML,ML-DFT,ML-ESM,MPNN,non-collinear,OpenMX,prediction from magnetic configuration,prediction of Hamiltonian matrix,prediction of Jij,skyrmions,SO(3),SOC,spin spiral,spin-dependent,SU(2),ternary systems,TRS},
+ file = {/Users/wasmer/Nextcloud/Zotero/Zhong et al_2023_Accelerating the electronic-structure calculation of magnetic systems by.pdf;/Users/wasmer/Zotero/storage/RJIQYZHY/2306.html}
+}
+
@online{zhouComprehensiveSurveyPretrained2023,
title = {A {{Comprehensive Survey}} on {{Pretrained Foundation Models}}: {{A History}} from {{BERT}} to {{ChatGPT}}},
shorttitle = {A {{Comprehensive Survey}} on {{Pretrained Foundation Models}}},
@@ -9872,6 +12045,23 @@
file = {/Users/wasmer/Nextcloud/Zotero/Zhou et al_2023_A Comprehensive Survey on Pretrained Foundation Models.pdf;/Users/wasmer/Zotero/storage/CWZ9H6CB/2302.html}
}
+@book{zhuBogoliubovdeGennesMethod2016,
+ title = {Bogoliubov-de {{Gennes Method}} and {{Its Applications}}},
+ author = {Zhu, Jian-Xin},
+ date = {2016},
+ series = {Lecture {{Notes}} in {{Physics}}},
+ volume = {924},
+ publisher = {{Springer International Publishing}},
+ location = {{Cham}},
+ doi = {10.1007/978-3-319-31314-6},
+ url = {http://link.springer.com/10.1007/978-3-319-31314-6},
+ urldate = {2023-05-03},
+ isbn = {978-3-319-31312-2 978-3-319-31314-6},
+ langid = {english},
+ keywords = {Andreev Reflection Process,Blonder-Tinkham-Klapwijk Theory,d-wave Superconductors,Distribution of Nonmagnetic Impurities,Green’s Function Method,High-Tc Cuprates,Kondo Coherence Order Parameter,Kondo Hole System,Majorana Fermions,Mesoscopic Superconductivity,Multi-Orbital SuperConductors,S-wave Superconductors,superconductor,Topological Kondo Insulator,Topological Superconductor,Transport Across Superconductor Junctions,Vortices in Superconductors},
+ file = {/Users/wasmer/Nextcloud/Zotero/Zhu_2016_Bogoliubov-de Gennes Method and Its Applications.pdf}
+}
+
@thesis{zimmermannInitioDescriptionTransverse2014,
title = {Ab Initio Description of Transverse Transport Due to Impurity Scattering in Transition-Metals},
author = {Zimmermann, Bernd},
@@ -9883,7 +12073,7 @@
abstract = {This thesis attempts to shed light on various spin-orbit driven transport phenomenain materials, as a crucial for the further development of the field of spintronics. Inparticular, we address the skew-scattering mechanism in dilute alloys, which gives rise to the anomalous and spin Hall effect, as well as spin-relaxation processes. We create the tools to access these quantities from \$\textbackslash textit\{ab initio\}\$ calculations in the framework of the full-potential all-electron Korringa-Kohn-Rostoker Green-function method, by (a) developing and implementing a new tetrahedron method for the calculation of complicated, multi-sheeted Fermi surfaces even of complex transition-metal compounds, and (b) developing an efficiently parallelized and thus highly scalable computer program (up to thousands of processors) for the precise calculation of scattering properties. In a first application of the new tetrahedron method, we calculate the Elliott-Yafet spin-mixing parameter on the Fermi surfaces of 5\$\textbackslash textit\{d\}\$ and 6\$\textbackslash textit\{sp\}\$ metals, and discover a yet unexplored dependence on the electron's spin-polarization direction. As we show, this anisotropy can reach gigantic values in uniaxial hcp crystals due to the emergenceof large spin-ip hot-areas or hot-loops on the Fermi surface, supported by the low symmetry of the hcp crystal. A simple model is able to reveal an interesting interplay between the orbital character of the states at special points, lines or areas in the Brillouin zone and the matrix-elements of the spin-flip part of the spin-orbit coupling operator. We further calculate the skew-scattering contribution to the anomalous Hall effect(AHE) in dilute alloys based on a ferromagnetic host for the first time. A systematic study of 3\$\textbackslash textit\{d\}\$ impurities in bcc Fe, as well as the non-magnetic hosts Pd, Pt and Au, allows us to identify trends across the periodic table. In all our calculations, we also observe a strong correlation between the spin Hall effect and anomalous Hall effect in these materials, which is of interest for the creation and detection of strongly spin-polarized currents. A Fermi-surface analysis of the contributions to the AHE reveals a non-trivial, peaked behavior at small hot-spots around spin-orbit lifted degeneracies. We then proceed to the more complicated \$\textbackslash textit\{L\}\$1\$\_\{0\}\$-ordered alloy FePt and address different kinds of disorder. We showcase the power of our method by treating the very complicated compounds Fe\$\_\{x\}\$Mn\$\_\{1-x\}\$Si and MnSi\$\_\{1-x\}\$Ge\$\_\{x\}\$, based on the non-Fermi liquid manganese silicide (MnSi). Finally, we also calculate the pure spin Hall effect for 4\$\textbackslash textit\{d\}\$/5\$\textbackslash textit\{sp\}\$ and 5\$\textbackslash textit\{d\}\$/6\$\textbackslash textit\{sp\}\$ impurities in fcc Ir and hcp Re hosts. For the latter, we discover a strong dependence on the electron's spin-polarization direction. Zimmermann, Bernd},
isbn = {9783893369850},
langid = {english},
- keywords = {_tablet,Boltzmann theory,Hall AHE,Hall effect,Hall SHE,juKKR,KKR,PGI-1/IAS-1,thesis,transport properties},
+ keywords = {\_tablet,Boltzmann theory,Hall AHE,Hall effect,Hall SHE,juKKR,KKR,PGI-1/IAS-1,thesis,transport properties},
file = {/Users/wasmer/Nextcloud/Zotero/Zimmermann_2014_Ab initio description of transverse transport due to impurity scattering in.pdf;/Users/wasmer/Zotero/storage/QL7I6VYG/171881.html}
}
--
GitLab