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:
-
[1];
[1];
[2];
- 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)
- 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 = {Mon Jul 16 00:00:00 EDT 2018},
month = {Mon Jul 16 00:00:00 EDT 2018}
}
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