DeePKS-kit: A package for developing machine learning-based chemically accurate energy and density functional models
Published: 10 October 2022| Version 1 | DOI: 10.17632/x54bnz5vxk.1
Contributors:
, , , Description
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.
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Categories
Computational Physics, Density Functional Theory, Electronic Structure, Deep Learning