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

Abstract

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

Authors:
ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [2];  [1];  [3]
  1. Princeton Univ., NJ (United States)
  2. Inst. of Applied Physics and Computational Mathematics (IAPCM), Beijing (China); CAEP Software CEnter for High Performance Numerical Simulation, Beijing (China)
  3. Princeton Univ., NJ (United States); Beijing Inst. of Big Data Research (China)
Publication Date:
Research Org.:
Princeton Univ., NJ (United States)
Sponsoring Org.:
USDOE Office of Science (SC)
OSTI Identifier:
1512932
Alternate Identifier(s):
OSTI ID: 1460500
Grant/Contract Number:  
SC0008626; SC0009248
Resource Type:
Accepted Manuscript
Journal Name:
Journal of Chemical Physics
Additional Journal Information:
Journal Volume: 149; Journal Issue: 3; Journal ID: ISSN 0021-9606
Publisher:
American Institute of Physics (AIP)
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING

Citation Formats

Zhang, Linfeng, Han, Jiequn, Wang, Han, Car, Roberto, and E, Weinan. DeePCG: Constructing coarse-grained models via deep neural networks. United States: N. p., 2018. Web. doi:10.1063/1.5027645.
Zhang, Linfeng, Han, Jiequn, Wang, Han, Car, Roberto, & E, Weinan. DeePCG: Constructing coarse-grained models via deep neural networks. United States. https://doi.org/10.1063/1.5027645
Zhang, Linfeng, Han, Jiequn, Wang, Han, Car, Roberto, and E, Weinan. Mon . "DeePCG: Constructing coarse-grained models via deep neural networks". United States. https://doi.org/10.1063/1.5027645. https://www.osti.gov/servlets/purl/1512932.
@article{osti_1512932,
title = {DeePCG: Constructing coarse-grained models via deep neural networks},
author = {Zhang, Linfeng and Han, Jiequn and Wang, Han and Car, Roberto and E, Weinan},
abstractNote = {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},
doi = {10.1063/1.5027645},
journal = {Journal of Chemical Physics},
number = 3,
volume = 149,
place = {United States},
year = {2018},
month = {7}
}

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