Simple approach to more efficient density functional theory simulations

Mohammed Benaissa*, Tarik Ouahrani, Keisuke Hatada, Kazuki Yoshikawa, Didier Sébilleau

*この論文の責任著者

研究成果: ジャーナルへの寄稿学術論文査読

抄録

In physics, chemistry and materials science, density functional theory (DFT) is a common method for studying the electronic structure of many-body systems. DFT can address complex materials with relatively low computational costs compared to other physics-based methods; however, it still requires considerable computational power for large-scale simulations. With the aim of reducing the computational footprint of DFT, we suggest the use of the data-efficient Bayesian algorithm to optimize the charge mixing parameters, which reduces the self-consistent field iterations necessary to reach convergence. We show that our algorithm can achieve faster convergence than the default parameters when the VASP code is used as a proof of concept, resulting in significant time savings in DFT simulations and therefore providing a systematic guide for more efficient DFT simulations. We propose adding this procedure to the well-known convergence test procedures, such as cutoff-energy and k-point convergence tests.

本文言語英語
論文番号e01012
ジャーナルComputational Condensed Matter
42
DOI
出版ステータス出版済み - 2025/03

ASJC Scopus 主題領域

  • 電子材料、光学材料、および磁性材料
  • 材料科学(その他)
  • 凝縮系物理学
  • 材料化学

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