DeePKS-kit: A package for developing machine learning-based chemically accurate energy and density functional models

Abstract

We introduce DeePKS-kit, an open-source software package for developing machine learning based energy and density functional models. DeePKS-kit is interfaced with PyTorch, an open-source machine learning library, and PySCF, an ab initio computational chemistry program that provides simple and customized tools for developing quantum chemistry codes. It supports the DeePHF and DeePKS methods. In addition to explaining the details in the methodology and the software, we also provide an example of developing a chemically accurate model for water clusters. Program summaryProgram Title: DeePKS-kitCPC Library link to program files:https://doi.org/10.17632/x54bnz5vxk.1Developer's repository link:https://github.com/deepmodeling/deepks-kitLicensing provisions: LGPLProgramming language: PythonNature of problem: Modeling the energy and density functional in electronic structure problems with high accuracy by neural network models. Solving electronic ground state energy and charge density using the learned model.Solution method: DeePHS and DeePKS methods are implemented, interfaced with PyTorch and PySCF for neural network training and self-consistent field calculations. An iterative learning procedure is included to train the model self-consistently.