diff --git a/bib/bibliography.bib b/bib/bibliography.bib
index 03389e0f6ea66497e741e09ee7c137dd48933ee1..8c5c75855230d491a66830bd2d324134ae2d6214 100644
--- a/bib/bibliography.bib
+++ b/bib/bibliography.bib
@@ -3829,7 +3829,7 @@ Subject\_term\_id: computational-methods;electronic-structure;theory-and-computa
urldate = {2025-01-08},
abstract = {Deep learning (DL) is one of the fastest-growing topics in materials data science, with rapidly emerging applications spanning atomistic, image-based, spectral, and textual data modalities. DL allows analysis of unstructured data and automated identification of features. The recent development of large materials databases has fueled the application of DL methods in atomistic prediction in particular. In contrast, advances in image and spectral data have largely leveraged synthetic data enabled by high-quality forward models as well as by generative unsupervised DL methods. In this article, we present a high-level overview of deep learning methods followed by a detailed discussion of recent developments of deep learning in atomistic simulation, materials imaging, spectral analysis, and natural language processing. For each modality we discuss applications involving both theoretical and experimental data, typical modeling approaches with their strengths and limitations, and relevant publicly available software and datasets. We conclude the review with a discussion of recent cross-cutting work related to uncertainty quantification in this field and a brief perspective on limitations, challenges, and potential growth areas for DL methods in materials science.},
langid = {english},
- keywords = {/unread,AML,CNN,Computational methods,generative models,GNN,list of software,lists,ML,NLP,reinforcement-learning,representation learning,review,review-of-AML,uncertainty quantification},
+ keywords = {AML,CNN,Computational methods,generative models,GNN,list of software,lists,ML,NLP,reinforcement-learning,representation learning,review,review-of-AML,uncertainty quantification},
file = {/Users/wasmer/Nextcloud/Zotero/Choudhary et al. - 2022 - Recent advances and applications of deep learning methods in materials science.pdf}
}
@@ -4069,7 +4069,7 @@ Subject\_term\_id: computational-methods;electronic-structure;theory-and-computa
urldate = {2024-09-30},
abstract = {The application of machine-learning techniques to atomistic modeling of physics, chemistry and materials science is blooming, and machine learning is becoming a},
langid = {english},
- keywords = {/unread,AML,collection,ML,review-of-AML,software,special issue},
+ keywords = {AML,collection,ML,review-of-AML,software,special issue},
file = {/Users/wasmer/Zotero/storage/V88JWUZY/Software-for-Atomistic-Machine-Learning.html}
}
@@ -16605,7 +16605,7 @@ Subject\_term\_id: condensed-matter-physics;history;quantum-physics},
doi = {10.1063/5.0228461},
url = {https://doi.org/10.1063/5.0228461},
urldate = {2024-09-30},
- keywords = {/unread,AML,best-of-list,ML,review-of-AML,software},
+ keywords = {AML,best-of-list,ML,review-of-AML,software},
file = {/Users/wasmer/Nextcloud/Zotero/Rupp et al. - 2024 - Guest editorial Special Topic on software for atomistic machine learning.pdf;/Users/wasmer/Zotero/storage/WDZTVFQU/Guest-editorial-Special-Topic-on-software-for.html}
}
@@ -20356,18 +20356,7 @@ Subject\_term\_id: electronic-properties-and-materials;quantum-hall;superconduct
file = {/Users/wasmer/Zotero/storage/28HAV2UB/10430514.html}
}
-@online{wasmerBestAtomisticMachine2023,
- title = {Best of {{Atomistic Machine Learning}}},
- author = {Wasmer, Johannes and Evans, Matthew and Blaiszik, Ben and Riebesell, Janosh},
- date = {2023-12-25},
- doi = {10.5281/zenodo.10934602},
- url = {https://doi.org/10.5281/zenodo.10934602},
- urldate = {2024-05-29},
- abstract = {A ranked list of awesome atomistic machine learning projects.},
- keywords = {/unread,ai4science,atomistic-machine-learning,awesome-list,best-of-list,chemistry-datasets,community-resource,computational-chemistry,computational-materials-science,density-functional-theory,drug-discovery,electronic-structure,interatomic-potentials,living-document,materials-datasets,materials-discovery,materials-informatics,molecular-dynamics,quantum-chemistry,scientific-machine-learning,surrogate-models}
-}
-
-@report{wasmerBestAtomisticMachine2023a,
+@report{wasmerBestAtomisticMachine2023,
title = {Best of {{Atomistic Machine Learning}}},
author = {Wasmer, Johannes and Riebesell, Janosh and Evans, Matthew and Blaiszik, Ben},
date = {2023},
@@ -20382,6 +20371,17 @@ Subject\_term\_id: electronic-properties-and-materials;quantum-hall;superconduct
file = {/Users/wasmer/Zotero/storage/7N6RJXIT/1020061.html}
}
+@online{wasmerBestAtomisticMachine2023a,
+ title = {Best of {{Atomistic Machine Learning}}},
+ author = {Wasmer, Johannes and Evans, Matthew and Blaiszik, Ben and Riebesell, Janosh},
+ date = {2023-12-25},
+ doi = {10.5281/zenodo.10934602},
+ url = {https://doi.org/10.5281/zenodo.10934602},
+ urldate = {2024-05-29},
+ abstract = {A ranked list of awesome atomistic machine learning projects.},
+ keywords = {/unread,ai4science,atomistic-machine-learning,awesome-list,best-of-list,chemistry-datasets,community-resource,computational-chemistry,computational-materials-science,density-functional-theory,drug-discovery,electronic-structure,interatomic-potentials,living-document,materials-datasets,materials-discovery,materials-informatics,molecular-dynamics,quantum-chemistry,scientific-machine-learning,surrogate-models}
+}
+
@unpublished{wasmerComparisonStructuralRepresentations2021,
type = {Poster},
title = {Comparison of Structural Representations for Machine Learning-Accelerated Ab Initio Calculations},
@@ -20397,20 +20397,6 @@ Subject\_term\_id: electronic-properties-and-materials;quantum-hall;superconduct
}
@thesis{wasmerDevelopmentSurrogateMachine2021,
- type = {mathesis},
- title = {Development of a Surrogate Machine Learning Model for the Acceleration of Density Functional Calculations with the {{Korringa-Kohn-Rostoker}} Method},
- author = {Wasmer, Johannes and Rüßmann, Philipp and Blügel, Stefan},
- date = {2021-10-27},
- institution = {RWTH Aachen University},
- url = {https://iffgit.fz-juelich.de/phd-project-wasmer/theses/master-thesis},
- urldate = {2022-08-08},
- langid = {english},
- pagetotal = {99},
- keywords = {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}
-}
-
-@thesis{wasmerDevelopmentSurrogateMachine2021a,
title = {Development of a Surrogate Machine Learning Model for the Acceleration of Density Functional Calculations with the {{Korringa-Kohn-Rostoker}} Method},
author = {Wasmer, Johannes and Berkels, Benjamin and Rüssmann, Philipp and Blügel, Stefan},
date = {2021},
@@ -20424,6 +20410,23 @@ Subject\_term\_id: electronic-properties-and-materials;quantum-hall;superconduct
file = {/Users/wasmer/Zotero/storage/NTW3FHQ6/1020053.html}
}
+@thesis{wasmerDevelopmentSurrogateMachine2021a,
+ type = {mathesis},
+ title = {Development of a Surrogate Machine Learning Model for the Acceleration of Density Functional Calculations with the {{Korringa-Kohn-Rostoker}} Method},
+ author = {Wasmer, Johannes and Rüssmann, Philipp and Berkels, Benjamin and Blügel, Stefan},
+ date = {2021},
+ number = {FZJ-2023-05854},
+ institution = {Masterarbeit, RWTH Aachen University, 2022},
+ location = {Aachen},
+ doi = {10.34734/FZJ-2023-05854},
+ url = {https://doi.org/10.34734/FZJ-2023-05854},
+ urldate = {2025-01-26},
+ abstract = {Density functional theory (DFT) has become an indispensable tool in materials science. Specialized DFT methods like the Korringa-Kohan Rostoker Green Function (KKR) method are predestined to investigate the technologically relevant effects of crystallographic defects on the electronic and magnetic structure of host materials. This thesis lays the groundwork for answering the question of whether surrogate machine learning (ML) models have the potential to accelerate such DFT calculations since their computational complexity severely limits them to systems sizes of about a thousand atoms in practice. To that end, a versatile suite of software tools that facilitates the generation and analysis of high-throughput computing DFT datasets with the JuKKR DFT codes and the AiiDA workflow engine is presented. We demonstrate its use by generating a database of 8,760 converged KKR DFT calculations of single impurity embeddings into elemental crystals with 60 different chemical elements and varying lattice constants and that preserves the full data provenance of each calculation. Finally, we use the single-impurity database to compare the Coulomb Matrix and the Smooth Overlap of Atomic Positions (SOAP) as structural descriptors of the local atomic environment for materials defects. Their potential use in surrogate ML models is showcased in a simple example of host crystal structure prediction that achieves 93 percent accuracy. Wasmer, Johannes; Blügel, Stefan; Rüssmann, Philipp; Berkels, Benjamin},
+ langid = {english},
+ keywords = {/unread,AiiDA,aiida-kkr,AML,database generation,dataset,descriptors,FZJ,HTC,impurity embedding,JuDFT,juKKR,KKR,ML,ML-Density,ML-DFT,periodic table,PGI,PGI-1,prediction of electron potential,RWTH,SOAP,thesis,transition metals,workflows},
+ file = {/Users/wasmer/Nextcloud/Zotero/Wasmer et al. - 2021 - Development of a surrogate machine learning model for the acceleration of density functional calcula.pdf;/Users/wasmer/Zotero/storage/N3VY83MP/1020053.html}
+}
+
@unpublished{wasmerPhysicsPhysicsAIHybrids2024,
type = {Conference talk},
title = {From Physics to Physics-{{AI}} Hybrids in Quantum Materials Simulation},