Details

Autor(en) / Beteiligte

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

Titel

DeePCG: Constructing coarse-grained models via deep neural networks

Ist Teil von

• The Journal of chemical physics, 2018-07, Vol.149 (3), p.034101-034101

Ort / Verlag

United States: American Institute of Physics

Erscheinungsjahr

2018

Quelle

American Institute of Physics (AIP) Journals

Beschreibungen/Notizen

• 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.