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Recent developments in many-body potential energy representation via deeplearning have brought new hopes to addressing the accuracy-versus-efficiencydilemma in molecular simulations. Here we describe DeePMD-kit, a packagewritten in Python/C++ that has been designed to minimize the effort required tobuild deep learning based representation of potential energy and force fieldand to perform molecular dynamics. Potential applications of DeePMD-kit spanfrom finite molecules to extended systems and from metallic systems tochemically bonded systems. DeePMD-kit is interfaced with TensorFlow, one of themost popular deep learning frameworks, making the training process highlyautomatic and efficient. On the other end, DeePMD-kit is interfaced withhigh-performance classical molecular dynamics and quantum (path-integral)molecular dynamics packages, i.e., LAMMPS and the i-PI, respectively. Thus,upon training, the potential energy and force field models can be used toperform efficient molecular simulations for different purposes. As an exampleof the many potential applications of the package, we use DeePMD-kit to learnthe interatomic potential energy and forces of a water model using dataobtained from density functional theory. We demonstrate that the resultedmolecular dynamics model reproduces accurately the structural informationcontained in the original model.

DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics