DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics
This provides a tool for researchers in computational chemistry and materials science to perform efficient molecular simulations, though it is incremental as it builds on existing deep learning methods for potential energy representation.
The authors tackled the accuracy-versus-efficiency dilemma in molecular simulations by developing DeePMD-kit, a deep learning package for many-body potential energy representation and molecular dynamics, demonstrating that it accurately reproduces structural information from density functional theory data for a water model.
Recent developments in many-body potential energy representation via deep learning have brought new hopes to addressing the accuracy-versus-efficiency dilemma in molecular simulations. Here we describe DeePMD-kit, a package written in Python/C++ that has been designed to minimize the effort required to build deep learning based representation of potential energy and force field and to perform molecular dynamics. Potential applications of DeePMD-kit span from finite molecules to extended systems and from metallic systems to chemically bonded systems. DeePMD-kit is interfaced with TensorFlow, one of the most popular deep learning frameworks, making the training process highly automatic and efficient. On the other end, DeePMD-kit is interfaced with high-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 to perform efficient molecular simulations for different purposes. As an example of the many potential applications of the package, we use DeePMD-kit to learn the interatomic potential energy and forces of a water model using data obtained from density functional theory. We demonstrate that the resulted molecular dynamics model reproduces accurately the structural information contained in the original model.