We introduce a scheme for molecular simulations, the deep potential molecular dynamics (DPMD) 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-principles 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, DPMD gives results that are essentially indistinguishable from the original data, at a cost that scales linearly with system size.

• Biological neural networks
• Classical statistical mechanics
• Density functional theory
• First-principles calculations
• Interatomic & molecular potentials
• Learning
• Potential energy surfaces
• Solid-state chemistry
• Thermal properties
• Van der Waals interaction
• Molecular dynamics