An accurate description of the interatomic potential energy surface (PES) is one of the central problems in molecular simulations. For a long time, one has to choose between the first principle PESs that are accurate but computationally expensive and the empirical PESs (force fields) that are efficient but of limited accuracy. We discuss the solution to this dilemma in two aspects: PES construction and data generation. In terms of PES construction, we introduce the Deep Potential (DP) method, which faithfully represents the first principle PES by a symmetry-preserving deep neural network. In terms of data generation, we present a new concurrent learning scheme named Deep Potential Generator (DP-GEN). This approach automatically generates the most compact training dataset that enables the training of DP with uniform accuracy. By contrast to the empirical PESs, the DP-GEN opens the opportunity of continuously improving the quality of DP by exploring the chemical and configurational space of the system. After a few examples of DP and DP-GEN, we introduce the open-source implementations of DP named DeePMD-kit, and a recent GPU optimization of DeePMD-kit for the world's fastest supercomputer, which makes possible nanosecond simulation of 100 million atoms with ab initio accuracy in a day.

Dr. Han Wang is currently an associate researcher at Institute of Applied Physics and Computational Mathematics, Beijing, China. Before moving to IAPCM, he was a postdoctoral researcher at Freie Universität Berlin. Dr. Wang received his Ph.D and B. S. in computational mathematics from Peking University, in 2011, and 2006, respectively. Dr Wang’s area is multiscale modeling and simulation, numerical analysis and fast algorithms for molecular dynamics. He has published more than 50 papers, and win Gordon Bell prize 2020.