Adaptive coupling of a deep neural network potential to a classical force field

Zhang, Linfeng; Wang, Han; E, Weinan

doi:10.1063/1.5042714

Title: Adaptive coupling of a deep neural network potential to a classical force field

Journal Article · Wed Oct 17 00:00:00 EDT 2018 · Journal of Chemical Physics

DOI:https://doi.org/10.1063/1.5042714· OSTI ID:1611098

^[1];

^[2]; E, Weinan ^[3]

Princeton Univ., NJ (United States)
Lab. of Computational Physics, Inst. of Applied Physics and Computational Mathematics, Beijing (China)
Princeton Univ., NJ (United States); Peking Univ., Beijing (China). Center for Data Science and Beijing International Center for Mathematical Research; Beijing Inst. of Big Data Research, Beijing (China)

An adaptive modeling method (AMM) that couples a deep neural network potential and a classical force field is introduced to address the accuracy-efficiency dilemma faced by the molecular simulation community. The AMM simulated system is decomposed into three types of regions. The first type captures the important phenomena in the system and requires high accuracy, for which we use the Deep Potential Molecular Dynamics (DeePMD) model in this work. The DeePMD model is trained to accurately reproduce the statistical properties of the ab initio molecular dynamics. The second type does not require high accuracy, and a classical force field is used to describe it in an efficient way. The third type is used for a smooth transition between the first and the second types of regions. By using a force interpolation scheme and imposing a thermodynamics force in the transition region, we make the DeePMD region embedded in the AMM simulated system as if it were embedded in a system that is fully described by the accurate potential. A representative example of the liquid water system is used to show the feasibility and promise of this method.

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Research Organization:: Princeton Univ., NJ (United States)

Sponsoring Organization:: USDOE Office of Science (SC); US Department of the Navy, Office of Naval Research (ONR); National Science Foundation of China (NSFC); National Key Research and Development Program of China

Grant/Contract Number:: SC0008626; SC0009248; N00014-13-1-0338; U1430237; 91530322; 11501039; 11871110; 2016YFB0201200; 2016YFB0201203

OSTI ID:: 1611098

Alternate ID(s):: OSTI ID: 1477902

Journal Information:: Journal of Chemical Physics, Vol. 149, Issue 15; ISSN 0021-9606

Publisher:: American Institute of Physics (AIP)Copyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 10 works

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