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

@article{Zhang2018DeePCGCC,
  title={DeePCG: Constructing coarse-grained models via deep neural networks.},
  author={Linfeng Zhang and Jiequn Han and Han Wang and Roberto Car and Weinan E},
  journal={The Journal of chemical physics},
  year={2018},
  volume={149 3},
  pages={
          034101
        }
}
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… 

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