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的两难困境带来了新的希望。本文介绍了DeePMD-kit：一款采用Python与C++编写的软件包，旨在降低构建基于深度学习的势能与力场表征、并开展分子动力学模拟所需的工作量。DeePMD-kit的潜在应用覆盖范围广泛，从有限分子体系到周期性延展体系，从金属体系到化学键合体系均有涉及。DeePMD-kit与当前最主流的深度学习框架之一TensorFlow（TensorFlow）实现了对接，可使训练过程高度自动化且高效。另一方面，DeePMD-kit还分别对接了高性能经典分子动力学与量子（路径积分）分子动力学软件包，即LAMMPS与i-PI。因此，在完成训练后，所得的势能与力场模型即可用于开展各类用途的高效分子模拟。作为该软件包众多潜在应用的一例，我们利用从密度泛函理论（density functional theory）获取的数据，借助DeePMD-kit学习了某一水模型的原子间势能与原子受力情况。结果表明，所得到的分子动力学模型能够精准复现原始模型所包含的结构信息。