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


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
Publication statusPublished - 2019 Nov 1


ASJC Scopus subject areas

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

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