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

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

研究成果: Article査読

11 被引用数 (Scopus)

抄録

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.

本文言語English
論文番号136732
ジャーナルChemical Physics Letters
734
DOI
出版ステータスPublished - 2019 11

ASJC Scopus subject areas

  • 物理学および天文学(全般)
  • 物理化学および理論化学

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