abstract = {We introduce prediction-powered inference \$\textbackslash unicode\{x2013\}\$ a framework for performing valid statistical inference when an experimental data set is supplemented with predictions from a machine-learning system. Our framework yields provably valid conclusions without making any assumptions on the machine-learning algorithm that supplies the predictions. Higher accuracy of the predictions translates to smaller confidence intervals, permitting more powerful inference. Prediction-powered inference yields simple algorithms for computing valid confidence intervals for statistical objects such as means, quantiles, and linear and logistic regression coefficients. We demonstrate the benefits of prediction-powered inference with data sets from proteomics, genomics, electronic voting, remote sensing, census analysis, and ecology.},
abstract = {Melting in the deep rocky portions of planets is important for understanding the thermal evolution of these bodies and the possible generation of magnetic fields in their underlying metallic cores. But the melting temperature of silicates is poorly constrained at the pressures expected in super-Earth exoplanets, the most abundant type of planets in the galaxy. Here, we propose an iterative learning scheme that combines enhanced sampling, feature selection, and deep learning, and develop a unified machine learning potential of ab initio quality valid over a wide pressure-temperature range to determine the melting temperature of MgSiO3. The melting temperature of the high-pressure, post-perovskite phase, important for super-Earths, increases more rapidly with increasing pressure than that of the lower pressure perovskite phase, stable at the base of Earth's mantle. The volume of the liquid closely approaches that of the solid phases at the highest pressure of our study. Our computed triple point constrains the Clapeyron slope of the perovskite to post-perovskite transition, which we compare with observations of seismic reflectivity at the base of Earth's mantle to calibrate Earth's core heat flux.},
keywords = {/unread,AIMD,AML,compositional descriptors,DeePMD-kit,DFT,iterative learning,iterative learning scheme,LAMMPS,materials,MD,MLP,prediction of total energy,SOAP,thermodynamics,VASP},
keywords = {AIMD,AML,compositional descriptors,DeePMD-kit,DFT,iterative learning,iterative learning scheme,LAMMPS,materials,MD,MLP,prediction of total energy,SOAP,thermodynamics,VASP},
file = {/Users/wasmer/Nextcloud/Zotero/Deng et al_2023_Melting of $-mathrm MgSi -mathrm O _ 3 $ determined by machine learning.pdf;/Users/wasmer/Zotero/storage/4DSIHJXI/PhysRevB.107.html}
abstract = {We show that the Gaussian Approximation Potential (GAP) machine-learning framework can describe complex magnetic potential energy surfaces, taking ferromagnetic iron as a paradigmatic challenging case. The training database includes total energies, forces, and stresses obtained from density-functional theory in the generalized-gradient approximation, and comprises approximately 150,000 local atomic environments, ranging from pristine and defected bulk configurations to surfaces and generalized stacking faults with different crystallographic orientations. We find the structural, vibrational, and thermodynamic properties of the GAP model to be in excellent agreement with those obtained directly from first-principles electronic-structure calculations. There is good transferability to quantities, such as Peierls energy barriers, which are determined to a large extent by atomic configurations that were not part of the training set. We observe the benefit and the need of using highly converged electronic-structure calculations to sample a target potential energy surface. The end result is a systematically improvable potential that can achieve the same accuracy of density-functional theory calculations, but at a fraction of the computational cost.},
file = {/Users/wasmer/Nextcloud/Zotero/Dragoni et al_2018_Achieving DFT accuracy with a machine-learning interatomic potential.pdf;/Users/wasmer/Zotero/storage/H8ISZZP6/PhysRevMaterials.2.html}
}
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@@ -3526,6 +3526,25 @@
file = {/Users/wasmer/Nextcloud/Zotero/Hafiz et al_2018_A high-throughput data analysis and materials discovery tool for strongly.pdf;/Users/wasmer/Zotero/storage/VAW7UFBH/s41524-018-0120-9.html}
}
@article{hafnerAbinitioSimulationsMaterials2008,
title = {Ab-Initio Simulations of Materials Using {{VASP}}: {{Density-functional}} Theory and Beyond},
shorttitle = {Ab-Initio Simulations of Materials Using {{VASP}}},
author = {Hafner, Jürgen},
date = {2008},
journaltitle = {Journal of Computational Chemistry},
file = {/Users/wasmer/Nextcloud/Zotero/Hafner_2008_Ab-initio simulations of materials using VASP.pdf;/Users/wasmer/Zotero/storage/UCE26HNQ/jcc.html}
}
@article{handleyNextGenerationInteratomic2014,
title = {Next Generation Interatomic Potentials for Condensed Systems},
author = {Handley, Christopher Michael and Behler, Jörg},
...
...
@@ -4978,6 +4997,24 @@
file = {/Users/wasmer/Nextcloud/Zotero/Liu et al_2017_Improving the Performance of Long-Range-Corrected Exchange-Correlation.pdf;/Users/wasmer/Zotero/storage/76EWRKPT/acs.jpca.html}
}
@article{liuMagneticTopologicalInsulator,
title = {Magnetic {{Topological Insulator Heterostructures}}: {{A Review}}},
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.},
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},
...
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@@ -5149,7 +5186,7 @@
urldate = {2023-01-20},
abstract = {The atomic cluster expansion (ACE) was proposed recently as a new class of data-driven interatomic potentials with a formally complete basis set. Since the development of any interatomic potential requires a careful selection of training data and thorough validation, an automation of the construction of the training dataset as well as an indication of a model's uncertainty are highly desirable. In this work, we compare the performance of two approaches for uncertainty indication of ACE models based on the D-optimality criterion and ensemble learning. While both approaches show comparable predictions, the extrapolation grade based on the D-optimality (MaxVol algorithm) is more computationally efficient. In addition, the extrapolation grade indicator enables an active exploration of new structures, opening the way to the automated discovery of rare-event configurations. We demonstrate that active learning is also applicable to explore local atomic environments from large-scale MD simulations.},
abstract = {Multi-principal element alloys (MPEAs) are a new class of alloys that consist of many principal elements randomly distributed on a crystal lattice. The random presence of many elements lends large variations in the point defect formation and migration energies even within a given alloy composition. Compounded by the fact that there could be exponentially large number of MPEA compositions, there is a major computational challenge to capture complete point-defect energy phase-space in MPEAs. In this work, we present a machine learning based framework in which the point defect energies in MPEAs are predicted from a database of their constituent binary alloys. We demonstrate predictions of vacancy migration and formation energies in face centered cubic ternary, quaternary and quinary alloys in Ni-Fe-Cr-Co-Cu system. A key benefit of building this framework based on the database of binary alloys is that it enables defect-energy predictions in alloy compositions that may be unearthed in future. Furthermore, the methodology enables identifying the impact of a given alloying element on the defect energies thereby enabling design of alloys with tailored defect properties.},
keywords = {AML,defects,descriptors,disordered,impurity embedding,LAMMPS,MD,ML,multi-principal element alloys,NEB,point defects,prediction from structure,prediction of energy,prediction of vacancy migration energy,supercell,TODO,vacancies},
file = {/Users/wasmer/Nextcloud/Zotero/Manzoor et al_2021_Machine Learning Based Methodology to Predict Point Defect Energies in.pdf}
}
@article{margrafPureNonlocalMachinelearned2021,
title = {Pure Non-Local Machine-Learned Density Functional Theory for Electron Correlation},
author = {Margraf, Johannes T. and Reuter, Karsten},
