The Rise of Neural Networks for Materials and Chemical Dynamics

Kulichenko, Maksim; Smith, Justin Steven; Nebgen, Benjamin Tyler; Li, Ying Wai; Fedik, Nikita Sergeyevich; Boldyrev, Alexander I.; Lubbers, Nicholas Edward; Barros, Kipton Marcos; Tretiak, Sergei

doi:10.1021/acs.jpclett.1c01357

Title: The Rise of Neural Networks for Materials and Chemical Dynamics

Journal Article · Thu Jul 01 00:00:00 EDT 2021 · Journal of Physical Chemistry Letters

DOI:https://doi.org/10.1021/acs.jpclett.1c01357· OSTI ID:1825448

Kulichenko, Maksim ^[1];

^[2];

^[2]; Li, Ying Wai ^[2]; Fedik, Nikita Sergeyevich ^[1];

^[3];

^[2];

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Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Utah State Univ., Logan, UT (United States)
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Utah State Univ., Logan, UT (United States)

Machine learning (ML) is quickly becoming a premier tool for modeling chemical processes and materials. ML-based force fields, trained on large data sets of high-quality electron structure calculations, are particularly attractive due their unique combination of computational efficiency and physical accuracy. This Perspective summarizes some recent advances in the development of neural network-based interatomic potentials. Designing high-quality training data sets is crucial to overall model accuracy. One strategy is active learning, in which new data are automatically collected for atomic configurations that produce large ML uncertainties. Another strategy is to use the highest levels of quantum theory possible. Transfer learning allows training to a data set of mixed fidelity. A model initially trained to a large data set of density functional theory calculations can be significantly improved by retraining to a relatively small data set of expensive coupled cluster theory calculations. These advances are exemplified by applications to molecules and materials.

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Research Organization:: Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

Sponsoring Organization:: USDOE Laboratory Directed Research and Development (LDRD) Program

Grant/Contract Number:: 89233218CNA000001

OSTI ID:: 1825448

Report Number(s):: LA-UR-21-23254

Journal Information:: Journal of Physical Chemistry Letters, Vol. 12, Issue 26; ISSN 1948-7185

Publisher:: American Chemical SocietyCopyright Statement

Country of Publication:: United States

Language:: English

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Graphics Processing Unit-Accelerated Semiempirical Born Oppenheimer Molecular Dynamics Using PyTorch Zhou, Guoqing; Nebgen, Ben; Lubbers, Nicholas Journal of Chemical Theory and Computation, Vol. 16, Issue 8 https://doi.org/10.1021/acs.jctc.0c00243	journal	July 2020
Extending the Applicability of the ANI Deep Learning Molecular Potential to Sulfur and Halogens Devereux, Christian; Smith, Justin S.; Davis, Kate K. Journal of Chemical Theory and Computation, Vol. 16, Issue 7 https://doi.org/10.1021/acs.jctc.0c00121	journal	June 2020

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