Semi-local machine-learned kinetic energy density functional demonstrating smooth potential energy curves

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

Research output: Contribution to journalArticle

Abstract

This letter investigates the accuracy of the semi-local machine-learned kinetic energy density functional (KEDF) for potential energy curves (PECs) in typical small molecules. The present functional is based on a previously developed functional adopting electron densities and their gradients up to the third order as descriptors (Seino et al., 2018). It further introduces new descriptors, namely, the distances between grid points and centers of nuclei, to describe the non-local nature of the KEDF. The numerical results show a reasonable performance of the present model in reproducing the PECs of small molecules with single, double, and triple bonds.

Original languageEnglish
Article number136732
JournalChemical Physics Letters
Volume734
DOIs
Publication statusPublished - 2019 Nov 1

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Potential energy
Kinetic energy
flux density
kinetic energy
potential energy
Molecules
curves
Carrier concentration
molecules
grids
gradients
nuclei

ASJC Scopus subject areas

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

Cite this

Semi-local machine-learned kinetic energy density functional demonstrating smooth potential energy curves. / Seino, Junji; Kageyama, Ryo; Fujinami, Mikito; Ikabata, Yasuhiro; Nakai, Hiromi.

In: Chemical Physics Letters, Vol. 734, 136732, 01.11.2019.

Research output: Contribution to journalArticle

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