抄録
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.
| 本文言語 | English |
|---|---|
| 論文番号 | 137358 |
| ジャーナル | Chemical Physics Letters |
| 巻 | 748 |
| DOI | |
| 出版ステータス | Published - 2020 6月 |
ASJC Scopus subject areas
- 物理学および天文学一般
- 物理化学および理論化学
フィンガープリント
「Orbital-free density functional theory calculation applying semi-local machine-learned kinetic energy density functional and kinetic potential」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。引用スタイル
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