We introduce a scheme for molecular simulations, the Deep Potential Molecular Dynamics (DeePMD) method, based on a many-body potential and interatomic forces generated by a carefully crafted deep neural network trained with ab initio data. The neural network model preserves all the natural symmetries in the problem. It is "first principle-based" in the sense that there are no ad hoc components aside from the network model. We show that the proposed scheme provides an efficient and accurate protocol in a variety of systems, including bulk materials and molecules. In all these cases, DeePMD gives results that are essentially indistinguishable from the original data, at a cost that scales linearly with system size.
@article{arxiv.1707.09571,
title = {Deep Potential Molecular Dynamics: a scalable model with the accuracy of quantum mechanics},
author = {Linfeng Zhang and Jiequn Han and Han Wang and Roberto Car and Weinan E},
journal= {arXiv preprint arXiv:1707.09571},
year = {2018}
}