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
