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We introduce a scheme for molecular simulations, the Deep Potential MolecularDynamics (DeePMD) method, based on a many-body potential and interatomic forcesgenerated by a carefully crafted deep neural network trained with ab initiodata. The neural network model preserves all the natural symmetries in theproblem. It is "first principle-based" in the sense that there are no ad hoccomponents aside from the network model. We show that the proposed schemeprovides an efficient and accurate protocol in a variety of systems, includingbulk materials and molecules. In all these cases, DeePMD gives results that areessentially indistinguishable from the original data, at a cost that scaleslinearly with system size.

Deep Potential Molecular Dynamics: a scalable model with the accuracy of quantum mechanics