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

TL;DR

DeePMD-kit is a Python/C++ package that simplifies creating deep learning-based potential energy models for molecular dynamics, enabling accurate and efficient simulations across various systems by interfacing with TensorFlow, LAMMPS, and i-PI.

Contribution

It introduces a user-friendly, integrated framework for developing and deploying deep learning potentials in molecular simulations, bridging quantum data and classical MD tools.

Findings

Accurately reproduces structural properties of water from DFT data.

Demonstrates versatility across different molecular and material systems.

Enhances efficiency of molecular dynamics simulations with deep learning potentials.

Abstract

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.