DeePKS: A Comprehensive Data-Driven Approach toward Chemically Accurate Density Functional Theory

Yixiao Chen, Linfeng Zhang, Han Wang, E. Weinan

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

We propose a general machine learning-based framework for building an accurate and widely applicable energy functional within the framework of generalized Kohn-Sham density functional theory. To this end, we develop a way of training self-consistent models that are capable of taking large datasets from different systems and different kinds of labels. We demonstrate that the functional that results from this training procedure gives chemically accurate predictions on energy, force, dipole, and electron density for a large class of molecules. It can be continuously improved when more and more data are available.

Original languageEnglish (US)
Pages (from-to)170-181
Number of pages12
JournalJournal of Chemical Theory and Computation
Volume17
Issue number1
DOIs
StatePublished - Jan 12 2021

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Physical and Theoretical Chemistry

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