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Keyword

machine learning interatomic potentials

Description: Computational models trained on data to approximate interatomic interactions and predict atomic forces and energies in materials and molecular simulations.
OpenAlex ID: keywords/machine-learning-interatomic-potentials
LAMMPS - a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales
2021 · Aidan P. Thompson, H. Metin Aktulga, et al. · Computer Physics Communications · 12,511
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Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons
2010 · Albert P. Bartók, Mike C. Payne, et al. · Physical Review Letters · 3,126
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Machine Learning Force Fields
2021 · Oliver T. Unke, Stefan Chmiela, et al. · Chemical Reviews · 1,626
Perspective: Machine learning potentials for atomistic simulations
2016 · Jörg Behler · The Journal of Chemical Physics · 1,451
Machine learning of accurate energy-conserving molecular force fields
2017 · Stefan Chmiela, Alexandre Tkatchenko, et al. · Science Advances · 1,178
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Spectral neighbor analysis method for automated generation of quantum-accurate interatomic potentials
2014 · A.P. Thompson, L.P. Swiler, et al. · Journal of Computational Physics · 1,130
Machine Learning Interatomic Potentials as Emerging Tools for Materials Science
2019 · Volker L. Deringer, Miguel A. Caro, et al. · Advanced Materials · 1,042
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Atomic cluster expansion for accurate and transferable interatomic potentials
2019 · Ralf Drautz · Physical review. B./Physical review. B · 960
CHGNet as a pretrained universal neural network potential for charge-informed atomistic modelling
2023 · Bowen Deng, Peichen Zhong, et al. · Nature Machine Intelligence · 930
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Performance and Cost Assessment of Machine Learning Interatomic Potentials
2020 · Yunxing Zuo, Chi Chen, et al. · The Journal of Physical Chemistry A · 920
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Four Generations of High-Dimensional Neural Network Potentials
2021 · Jörg Behler · Chemical Reviews · 877
Constructing high‐dimensional neural network potentials: A tutorial review
2015 · Jörg Behler · International Journal of Quantum Chemistry · 862
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Less is more: Sampling chemical space with active learning
2018 · Justin S. Smith, Ben Nebgen, et al. · The Journal of Chemical Physics · 807
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Comparing molecules and solids across structural and alchemical space
2016 · Sandip De, Albert P. Bartók, et al. · Physical Chemistry Chemical Physics · 774
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Towards exact molecular dynamics simulations with machine-learned force fields
2018 · Stefan Chmiela, Huziel E. Sauceda, et al. · Nature Communications · 771
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First Principles Neural Network Potentials for Reactive Simulations of Large Molecular and Condensed Systems
2017 · Jörg Behler · Angewandte Chemie International Edition · 708
Local Treatment of Electron Correlation
1993 · S Saebo, P Pulay · Annual Review of Physical Chemistry · 697
On-the-fly machine learning force field generation: Application to melting points
2019 · Ryosuke Jinnouchi, Ferenc Karsai, et al. · Physical review. B./Physical review. B · 682
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Active learning of linearly parametrized interatomic potentials
2017 · Evgeny V. Podryabinkin, Alexander V. Shapeev · Computational Materials Science · 678
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Machine learning based interatomic potential for amorphous carbon
2017 · Volker L. Deringer, Gábor Csányi · Physical review. B./Physical review. B · 669
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Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning
2019 · Justin S. Smith, Benjamin T. Nebgen, et al. · Nature Communications · 666
Molecular Dynamics with On-the-Fly Machine Learning of Quantum-Mechanical Forces
2015 · Zhenwei Li, James R. Kermode, et al. · Physical Review Letters · 632
Phase Transitions of Hybrid Perovskites Simulated by Machine-Learning Force Fields Trained on the Fly with Bayesian Inference
2019 · Ryosuke Jinnouchi, Jonathan Lahnsteiner, et al. · Physical Review Letters · 630
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Exceptional piezoelectricity, high thermal conductivity and stiffness and promising photocatalysis in two-dimensional MoSi2N4 family confirmed by first-principles
2020 · Bohayra Mortazavi, Brahmanandam Javvaji, et al. · Nano Energy · 628
DeePMD-kit v2: A software package for deep potential models
2023 · Jinzhe Zeng, Duo Zhang, et al. · The Journal of Chemical Physics · 619
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Works count: 13,990
Citation count: 132,600
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