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Machine-learning interatomic potentials have revolutionized materials modeling at the atomic scale.
doi: 10.1103/physrev.136.b864
Hohenberg P, Kohn W. Inhomogeneous Electron Gas. Phys. Rev. · 1964
Earlier work this paper cites.
doi: 10.1103/physrev.140.a1133
Kohn W, Sham LJ. Self-Consistent Equations Including Exchange and Correlation Effects. Phys. Rev. · 1965
Earlier work this paper cites.
Tersoff J. New empirical approach for the structure and energy of covalent systems. Physical review B
1988
Earlier work this paper cites.
Payne MC, Teter MP, Allan DC, Arias T, Joannopoulos aJ. Iterative minimization techniques for ab initio total-energy calculations: molecular dynamics and conjugate gradients. Reviews of modern physics
1992
Earlier work this paper cites.
doi: 10.1103/PhysRevB.50.17953
Blöchl PE. Projector augmented-wave method. Phys. Rev. B · 1994
Earlier work this paper cites.
doi: 10.1103/physrevlett.77.3865
Perdew JP, Burke K, Ernzerhof M. Generalized Gradient Approximation Made Simple. Physical Review Letters · 1996
Earlier work this paper cites.
doi: 10.1103/physrevb.55.10355
Gonze X, Lee C. Dynamical matrices, Born effective charges, dielectric permittivity tensors, and interatomic force constants from density-functional perturbation theory. Phys. Rev. B · 1997
Earlier work this paper cites.
doi: 10.1103/PhysRevB.59.1758
Kresse G, Joubert D. From ultrasoft pseudopotentials to the projector augmented-wave method. Phys. Rev. B · 1999
Earlier work this paper cites.
doi: 10.1103/revmodphys.73.515
Baroni S, Gironcoli dS, Dal Corso A, Giannozzi P. Phonons and related crystal properties from density-functional perturbation theory. Rev. Mod. Phys. · 2001
Earlier work this paper cites.
Reed J, Ceder G. Role of electronic structure in the susceptibility of metastable transition-metal oxide structures to transformation. Chemical reviews
2004
Earlier work this paper cites.
doi: 10.1103/physrevlett.98.146401
Behler J, Parrinello M. Generalized Neural-Network Representation of High-Dimensional Potential-Energy Surfaces. Physical Review Letters · 2007
Earlier work this paper cites.
Perdew JP, Ruzsinszky A, Csonka GI, et al. Restoring the density-gradient expansion for exchange in solids and surfaces. Physical review letters
2008
Earlier work this paper cites.
doi: 10.1088/0953-8984/22/2/022201
Klimeš J, Bowler DR, Michaelides A. Chemical accuracy for the van der Waals density functional. Journal of Physics: Condensed Matter · 2009
Earlier work this paper cites.
doi: 10.1103/physrevlett.104.136403
Bartók AP, Payne MC, Kondor R, Csányi G. Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons. Physical Review Letters · 2010
Earlier work this paper cites.
doi: 10.1016/j.commatsci.2010.05.010
Setyawan W, Curtarolo S. High-throughput electronic band structure calculations: Challenges and tools. Computational Materials Science · 2010
Earlier work this paper cites.
Behler J. Neural network potential-energy surfaces in chemistry: a tool for large-scale simulations. Physical Chemistry Chemical Physics
2011
Earlier work this paper cites.
doi: 10.1103/physrevb.84.045115
Jain A, Hautier G, Ong SP, et al. Formation enthalpies by mixing GGA and GGA calculations. Physical Review B · 2011
Earlier work this paper cites.
doi: 10.1016/j.commatsci.2012.10.028
Ong SP, Richards WD, Jain A, et al. Python Materials Genomics (pymatgen): A robust, open-source python library for materials analysis. Computational Materials Science · 2012
Earlier work this paper cites.
doi: 10.1063/1.4812323
Jain A, Ong SP, Hautier G, et al. Commentary: The Materials Project: A materials genome approach to accelerating materials innovation. APL Materials · 2013
Earlier work this paper cites.
doi: 10.1103/physrevb.88.085117
Hamann DR. Optimized norm-conserving Vanderbilt pseudopotentials. Phys. Rev. B · 2013
Earlier work this paper cites.
doi: 10.1016/j.jcp.2014.12.018
Thompson A, Swiler L, Trott C, Foiles S, Tucker G. Spectral neighbor analysis method for automated generation of quantum-accurate interatomic potentials. Journal of Computational Physics · 2014
Earlier work this paper cites.
Lejaeghere K, Bihlmayer G, Björkman T, et al. Reproducibility in density functional theory calculations of solids. Science
2016
Earlier work this paper cites.
Behler J. First principles neural network potentials for reactive simulations of large molecular and condensed systems. Angewandte Chemie International Edition
2017
Earlier work this paper cites.
doi: 10.1039/c6sc05720a
Smith JS, Isayev O, Roitberg AE. ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost. Chemical Science · 2017
Earlier work this paper cites.
doi: 10.1088/1361-648x/aa680e
Hjorth Larsen A, Jørgen Mortensen J, Blomqvist J, et al. The atomic simulation environment—a Python library for working with atoms. Journal of Physics: Condensed Matter · 2017
Earlier work this paper cites.
doi: 10.1063/1.5019779
Schütt KT, Sauceda HE, Kindermans PJ, Tkatchenko A, Müller KR. SchNet – A deep learning architecture for molecules and materials. The Journal of Chemical Physics · 2018
Earlier work this paper cites.
doi: 10.1038/sdata.2018.65
Petretto G, Dwaraknath S, P.C. Miranda H, et al. High-throughput density-functional perturbation theory phonons for inorganic materials. Sci. Data · 2018
Earlier work this paper cites.
doi: 10.1016/j.cpc.2018.01.012
Setten vM, Giantomassi M, Bousquet E, et al. The PseudoDojo: Training and grading a 85 element optimized norm-conserving pseudopotential table. Comput. Phys. Commun. · 2018
Earlier work this paper cites.
doi: 10.1002/adma.201902765
Deringer VL, Caro MA, Csányi G. Machine Learning Interatomic Potentials as Emerging Tools for Materials Science. Advanced Materials · 2019
Earlier work this paper cites.
doi: 10.1103/physrevb.99.014104
Drautz R. Atomic cluster expansion for accurate and transferable interatomic potentials. Physical Review B · 2019
Cited alongside, same era.
doi: 10.1021/acs.chemmater.9b01294
Chen C, Ye W, Zuo Y, Zheng C, Ong SP. Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals. Chemistry of Materials · 2019
Cited alongside, same era.
doi: 10.1038/s41467-019-10827-4
Smith JS, Nebgen BT, Zubatyuk R, et al. Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning. Nature Communications · 2019
Cited alongside, same era.
doi: 10.1126/sciadv.aav6490
Zubatyuk R, Smith JS, Leszczynski J, Isayev O. Accurate and transferable multitask prediction of chemical properties with an atoms-in-molecules neural network. Science Advances · 2019
Cited alongside, same era.
doi: 10.1016/j.cpc.2019.107042
Gonze X, Amadon B, Antonius G, et al. The Abinit project: Impact, environment and recent developments. Computer Physics Communications · 2019
Cited alongside, same era.
2022
Later among the works it cites.
Dusson G, Bachmayr M, Csányi G, et al. Atomic cluster expansion: Completeness, efficiency and stability. Journal of Computational Physics
2022
Later among the works it cites.
10.5281/zenodo.7486816 - https://github.com/janosh/pymatviz
Riebesell J. Pymatviz: visualization toolkit for materials informatics. Web Page; 2022 · 2022
Later among the works it cites.
Elsevier, 2023
Frenkel D, Smit B. Understanding molecular simulation: from algorithms to applications · 2023
Later among the works it cites.
doi: 10.1038/s43588-023-00561-9
Ko TW, Ong SP. Recent advances and outstanding challenges for machine learning interatomic potentials. Nature Computational Science · 2023
Later among the works it cites.
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2019
Cited alongside, same era.
Zuo Y, Chen C, Li X, et al. Performance and cost assessment of machine learning interatomic potentials. The Journal of Physical Chemistry A
2020
Cited alongside, same era.
doi: 10.1038/s41467-020-18556-9
Lilienfeld vOA, Burke K. Retrospective on a decade of machine learning for chemical discovery. Nature Communications · 2020
Cited alongside, same era.
doi: 10.1038/s41524-020-00440-1
Choudhary K, Garrity KF, Reid ACE, et al. The joint automated repository for various integrated simulations (JARVIS) for data-driven materials design. npj Computational Materials · 2020
Cited alongside, same era.
doi: 10.1063/1.5144261
Romero AH, Allan DC, Amadon B, et al. ABINIT: Overview and focus on selected capabilities. The Journal of Chemical Physics · 2020
Cited alongside, same era.
Wu Z, Pan S, Chen F, Long G, Zhang C, Philip SY. A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems
2020
Cited alongside, same era.
Mueller T, Hernandez A, Wang C. Machine learning for interatomic potential models. The Journal of chemical physics
2020
Cited alongside, same era.
Deng B, Zhong P, Jun K, et al. CHGNet as a pretrained universal neural network potential for charge-informed atomistic modelling. Nature Machine Intelligence · 2023
Later among the works it cites.
doi: 10.1039/d2dd00096b
Choudhary K, DeCost B, Major L, Butler K, Thiyagalingam J, Tavazza F. Unified graph neural network force-field for the periodic table: solid state applications. Digital Discovery · 2023
Later among the works it cites.
2023
Later among the works it cites.
doi: 10.1038/s41586-023-06735-9
Merchant A, Batzner S, Schoenholz SS, Aykol M, Cheon G, Cubuk ED. Scaling deep learning for materials discovery. Nature · 2023
Later among the works it cites.
doi: 10.1103/physrevmaterials.7.045802
Lopanitsyna N, Fraux G, Springer MA, De S, Ceriotti M. Modeling high-entropy transition metal alloys with alchemical compression. Physical Review Materials · 2023
Later among the works it cites.
2023
Later among the works it cites.
doi: 10.1038/s42254-023-00655-3
Bosoni E, Beal L, Bercx M, et al. How to verify the precision of density-functional-theory implementations via reproducible and universal workflows. Nature Reviews Physics · 2023
Later among the works it cites.
doi: 10.1088/1361-648X/acd831
Togo A, Chaput L, Tadano T, Tanaka I. Implementation strategies in phonopy and phono3py. J. Phys. Condens. Matter · 2023
Later among the works it cites.
doi: 10.7566/JPSJ.92.012001
Togo A. First-principles Phonon Calculations with Phonopy and Phono3py. J. Phys. Soc. Jpn. · 2023
Later among the works it cites.
Accessed: 2024-02-29
https://github.com/materialsvirtuallab/matgl ; · 2024
Closest in time.
Qi J, Ko TW, Wood BC, Pham TA, Ong SP. Robust training of machine learning interatomic potentials with dimensionality reduction and stratified sampling. npj Computational Materials
2024
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2024
Closest in time.
2024
Closest in time.
Accessed: 2024-06-15
Materials Project. https://docs.materialsproject.org/methodology/materials-methodology/calculation-details/gga+u-calculations/hubbard-u-values ; · 2024
Closest in time.
Accessed: 2024-06-15
CHGNet pretrained. https://github.com/CederGroupHub/chgnet/blob/main/chgnet/pretrained/0.3.0/chgnet_0.3.0_e29f68s314m37.pth.tar ; · 2024
Closest in time.
Accessed: 2024-06-15
Materials Project Trajectory (MPtrj) dataset. figshare. https://doi.org/10.6084/m9.figshare.23713842 ; · 2024
Closest in time.
Accessed: 2024-06-15
M3GNet-DIRECT pretrained. https://github.com/materialsvirtuallab/matgl/tree/main/pretrained_models/M3GNet-MP-2021.2.8-DIRECT-PES ; · 2024
Closest in time.
Accessed: 2024-06-15
ACEsuit/mace-mp. https://github.com/ACEsuit/mace-mp/releases/tag/mace_mp_0 ; · 2024
Closest in time.
Accessed: 2024-06-15
ACEsuit/mace-mp. https://github.com/ACEsuit/mace/blob/main/mace/calculators/foundations_models.py ; · 2024
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Accessed: 2024-06-15
ALIGNN pretrained. https://github.com/usnistgov/alignn/blob/main/alignn/ff/ff.py ; · 2024
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Accessed: 2024-06-15
ALIGNN pretrained. https://figshare.com/ndownloader/files/41583594 ; · 2024
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Accessed: 2024-06-15
ALIGNN pretrained. https://github.com/usnistgov/alignn/blob/main/alignn/models/alignn_atomwise.py ; · 2024
Closest in time.
Accessed: 2024-06-24
Pymatgen Compatibility corrections entry. https://github.com/materialsproject/pymatgen/blob/master/pymatgen/entries/compatibility.py ; · 2024
Closest in time.