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Machine learning potentials

D Anstine, O Isayev, Machine Learning Interatomic Potentials and Long-Range Physics, JPCA 127, 2417 (2023)

P Friederich, F Hase, J Proppe, A Aspuru-Guzik, Machine-learned potentials for next-generation matter simulations, Nat Mater 20, 750 (2021)

V L Deringer, M A Caro, G Csanyi, Machine Learning Interatomic Potentials as Emerging Tools for Materials Science, AM 2019, 1902765 (2019)


R Jinnouchi, F Karsai, G Kresse, On-the-fly machine learning force field generation: Application to melting points, PRB 100, 014105 (2019)

W Pronobis, A Tkatchenko, K Muller, Many-Body Descriptors for Predicting Molecular Properties with Machine Learning: Analysis of Pairwise and Three-Body Interactions in Molecules, JCTC 14, 2991 (2018)

S J Smith, B T Nebgen, R Zubatyuk, N Lubbers, C Devereux, K Barros, S Tretiak, O Isayev, A Roitberg, Outsmarting Quantum Chemistry Through Transfer Learning, 10.26434/chemrxiv.6744440 (2018)

J S Smith, O Isayev, A E Roitberg, ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost, Chem Sci 8, 3192 (2017)

S Chmiela, A Tkatchenko, H Sauceda, I Poltavsky, K Schutt, K Muller, Machine learning of accurate energy-conserving molecular force fields, Sci Adv 3, e1603015 (2017)

Z Li, J R Kermode, A De Vita, Molecular Dynamics with On-the-Fly Machine Learning of Quantum-Mechanical Forces, PRL 114, 096405 (2015)

J Behler, Representing potential energy surfaces by high-dimensional neural network potentials, JPC 26, 183001 (2014)

A P Bartok, M C Payne, R Kondor, G Csanyi, Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons, PRL 104, 136403 (2010)