Department of Applied Physics and Physico-Informatics · Keio University 慶應義塾大学 理工学部 物理情報工学科

Predicting materials
from first principles.
第一原理から
物質を予測する。

We develop theory and computational methods for the electronic structure of solids. Our central question is how accurately the properties of superconductors and semiconductors can be reproduced on a computer, and we work on it at every level — the underlying theory, the algorithms, and the code itself. The aim is to widen the range of materials that lie within genuine computational reach. 固体の電子状態に対する理論・計算手法を開発しています。超伝導体や半導体の物性をコンピュータ上でいかに精密に再現するかという問題に、基礎理論・アルゴリズム・プログラムの開発からアプローチし、人間が計算できる材料の領域を広げていくことを目指します。

Our research研究内容

Newsお知らせ

  • Opened the website. ウェブサイトを公開しました。
  • The group has launched 研究室が発足しました。

Selected publications主な論文

  1. R. Akashi, “Electronic structure theory of H3S: Plane-wave-like valence states, density-of-states peak and its guaranteed proximity to the Fermi level.” Ann. Phys. (Berlin) 538, e00003 (2026)
  2. R. Akashi and H. Shinaoka, “Uniform electron benchmark for the first-principles GW0-Eliashberg theory.” Phys. Rev. B 113, 014507 (2026)
  3. C. Homma, R. Akashi, Y. Nishiya, S. Tsuneyuki, and Y.-I. Matsushita, “Practical rule for trimming the DFT-1/2 self-interaction: Band gap, absolute band alignment, and defect properties of semiconductors.” Phys. Rev. B 112, 115111 (2025)
  4. S. Lu, M. Kawamura, R. Akashi, and S. Tsuneyuki, “Assessing the superconductivity in the doped perovskite hydride KMgH3: Effects of lattice anharmonicity and spin fluctuations.” Phys. Rev. B 111, 134516 (2025)

Preprintプレプリント

  1. R. Akashi, M. Sogal, and K. Burke, “Can machines learn density functionals? Past, present, and future of ML in DFT.” To appear in Machine Learning in Condensed Matter Physics (Springer Series in Solid-State Sciences), 2025 Machine Learning in Condensed Matter Physics (Springer Series in Solid-State Sciences)所収予定, 2025

All publications →論文一覧 →