Python(编程语言)
计算机科学
软件
人工神经网络
计算科学
人工智能
机器学习
程序设计语言
作者
Yixiao Chen,Linfeng Zhang,Han Wang,E Weinan
标识
DOI:10.1016/j.cpc.2022.108520
摘要
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 Title: DeePKS-kit CPC Library link to program files: https://doi.org/10.17632/x54bnz5vxk.1 Developer's repository link: https://github.com/deepmodeling/deepks-kit Licensing provisions: LGPL Programming language: Python Nature 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.
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