TY - JOUR
T1 - Orbital-free density functional theory calculation applying semi-local machine-learned kinetic energy density functional and kinetic potential
AU - Fujinami, Mikito
AU - Kageyama, Ryo
AU - Seino, Junji
AU - Ikabata, Yasuhiro
AU - Nakai, Hiromi
N1 - Publisher Copyright:
© 2020 Elsevier B.V.
PY - 2020/6
Y1 - 2020/6
N2 - 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.
AB - 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.
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U2 - 10.1016/j.cplett.2020.137358
DO - 10.1016/j.cplett.2020.137358
M3 - Article
AN - SCOPUS:85082101479
SN - 0009-2614
VL - 748
JO - Chemical Physics Letters
JF - Chemical Physics Letters
M1 - 137358
ER -