Orbital-free density functional theory calculation applying semi-local machine-learned kinetic energy density functional and kinetic potential

Mikito Fujinami, Ryo Kageyama, Junji Seino, Yasuhiro Ikabata, Hiromi Nakai

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

This letter proposes a scheme of orbital-free density functional theory (OF-DFT) calculation for optimizing electron density based on a semi-local machine-learned (ML) kinetic energy density functional (KEDF). The electron density, which is represented by the square of the linear combination of Gaussian functions, is optimized using derivatives of electronic energy including ML kinetic potential (KP). The numerical assessments confirmed the accuracy of optimized density and total energy for atoms and small molecules obtained by the present scheme based on ML-KEDF and ML-KP.

Original languageEnglish
Article number137358
JournalChemical Physics Letters
Volume748
DOIs
Publication statusPublished - 2020 Jun

ASJC Scopus subject areas

  • Physics and Astronomy(all)
  • Physical and Theoretical Chemistry

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