Skip to main content

DeePCG: Constructing coarse-grained models via deep neural networks

Author(s): Zhang, Linfeng; Han, Jiequn; Wang, Han; Car, Roberto; E, Weinan

To refer to this page use:
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.
Publication Date: 16-Jul-2018
Electronic Publication Date: 16-Jul-2018
Citation: Zhang, Linfeng, Han, Jiequn, Wang, Han, Car, Roberto, E, Weinan. (2018). DeePCG: Constructing coarse-grained models via deep neural networks. The Journal of Chemical Physics, 149 (3), 10.1063/1.5027645
DOI: doi:10.1063/1.5027645
ISSN: 0021-9606
EISSN: 1089-7690
Language: en
Type of Material: Journal Article
Journal/Proceeding Title: The Journal of Chemical Physics
Version: Author's manuscript

Items in OAR@Princeton are protected by copyright, with all rights reserved, unless otherwise indicated.