DeePCG: Constructing coarse-grained models via deep neural networks

Zhang, Linfeng; Han, Jiequn; Wang, Han; Car, Roberto; E, Weinan

doi:10.1063/1.5027645

Title: DeePCG: Constructing coarse-grained models via deep neural networks

Journal Article · Mon Jul 16 00:00:00 EDT 2018 · Journal of Chemical Physics

DOI:https://doi.org/10.1063/1.5027645· OSTI ID:1512932

^[1];

^[2]; Car, Roberto ^[1]; E, Weinan ^[3]

Princeton Univ., NJ (United States)
Inst. of Applied Physics and Computational Mathematics (IAPCM), Beijing (China); CAEP Software CEnter for High Performance Numerical Simulation, Beijing (China)
Princeton Univ., NJ (United States); Beijing Inst. of Big Data Research (China)

We introduce a general framework for constructing coarse-grained potential models without ad hoc approximations such as limiting the potential to two- and/or three-body contributions. The scheme, called the Deep Coarse-Grained Potential (abbreviated DeePCG), exploits a carefully crafted neural network to construct a many-body coarse-grained potential. The network is trained with full atomistic data in a way that preserves the natural symmetries of the system. The resulting model is very accurate and can be used to sample the configurations of the coarse-grained variables in a much faster way than with the original atomistic model. As an application, we consider liquid water and use the oxygen coordinates as the coarse-grained variables, starting from a full atomistic simulation of this system at the ab initio molecular dynamics level. We find that the two-body, three-body, and higher-order oxygen correlation functions produced by the coarse-grained and full atomistic models agree very well with each other, illustrating the effectiveness of the DeePCG model on a rather challenging task

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

Sponsoring Organization:: USDOE Office of Science (SC)

Grant/Contract Number:: SC0008626; SC0009248

OSTI ID:: 1512932

Alternate ID(s):: OSTI ID: 1460500

Journal Information:: Journal of Chemical Physics, Vol. 149, Issue 3; ISSN 0021-9606

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

Country of Publication:: United States

Language:: English

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Cited by: 109 works

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