4.6 Article

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

期刊

CHEMICAL PHYSICS LETTERS
卷 748, 期 -, 页码 -

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ELSEVIER
DOI: 10.1016/j.cplett.2020.137358

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  1. Elements Strategy Initiative for Catalysts & Batteries (ESICB) project - Ministry of Education, Culture, Sports, Science, and Technology (MEXT), Japan [JPMXP0112101003]
  2. Japan Science and Technology Agency (JST)
  3. JSPS (KAKENHI) [JP18K14184]

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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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