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

[Display omitted] •A scheme for orbital-free density functional theory calculation is implemented.•The optimized density and electronic energy are evaluated.•Machine-learned kinetic energy density functional and kinetic potential are employed. This letter proposes a scheme of orbital-free density fu...

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Veröffentlicht in:Chemical physics letters 2020-06, Vol.748, p.137358, Article 137358
Hauptverfasser: Fujinami, Mikito, Kageyama, Ryo, Seino, Junji, Ikabata, Yasuhiro, Nakai, Hiromi
Format: Artikel
Sprache:eng
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Zusammenfassung:[Display omitted] •A scheme for orbital-free density functional theory calculation is implemented.•The optimized density and electronic energy are evaluated.•Machine-learned kinetic energy density functional and kinetic potential are employed. 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.
ISSN:0009-2614
1873-4448
DOI:10.1016/j.cplett.2020.137358