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

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.

近年来，基于深度学习的多体势能表征领域取得的新进展，为解决分子模拟中精度与效率难以兼顾的两难困境带来了新的希望。本文介绍了DeePMD-kit：一款基于Python与C++开发的工具包，旨在降低构建基于深度学习的势能与力场表征模型，并开展分子动力学模拟所需的开发工作量。DeePMD-kit的潜在应用场景覆盖有限分子体系至扩展体系、金属体系至化学键合体系等多类体系。该工具包与当前主流深度学习框架之一TensorFlow（TensorFlow）对接，可使训练过程高度自动化且高效。另一方面，DeePMD-kit分别对接了高性能经典分子动力学与量子（路径积分）分子动力学工具包LAMMPS（LAMMPS）与i-PI（i-PI）。因此，在完成训练后，所得的势能与力场模型可用于开展各类场景下的高效分子模拟。以该工具包的众多潜在应用之一为例，本文利用密度泛函理论（density functional theory）生成的数据集，借助DeePMD-kit学习了某水分子模型的原子间势能与受力情况，并证明所得分子动力学模型可准确复现原始模型所包含的结构信息。