Journal papers (peer-reviewed)

  1. Tomoei Takahashi, Takashi Takahashi, and Yoshiyuki Kabashima,
    Dynamical Regimes of Discrete Diffusion Models (36 pages, 7 figures) (link),
    Journal of Statistical Mechanics: Theory and Experiment (2026) 083402 (Published 26 August, 2026),
    DOI: 10.1088/1742-5468/ae9930,
    arXiv:2604.10961
  2. Tomoei Takahashi, George Chikenji, Kei Tokita, and Yoshiyuki Kabashima,
    Alpha helices are more evolutionarily robust to environmental perturbations than beta sheets: Bayesian learning and statistical mechanics for protein evolution (16 pages, 6 figures) (link),
    Physical Review Research 7, 023115 (Published 5 May, 2025),
    DOI: https://doi.org/10.1103/PhysRevResearch.7.023115,
    arXiv:2409.03297
  3. Tomoei Takahashi, George Chikenji, and Kei Tokita,
    The cavity method to protein design problem (14 pages, 2 figures) (link),
    Jounal of Statistical Mechanics: Theory and Experiment (2022) 103403 (Published 12 October, 2022),
    DOI: 10.1088/1742-5468/ac9465,
    arXiv:2205.03696
    Press Release (In Japanese)
  4. Tomoei Takahashi, George Chikenji, and Kei Tokita,
    Lattice protein design using Bayesian learning (11 pages, 8 figures) (link),
    Physical Review E 104, 014404 (Published 8 July, 2021),
    DOI: https://doi.org/10.1103/PhysRevE.104.014404,
    arXiv:2003.06601
    Press Release (In Japanese)

Invited airticles (society magazine, editorial reviewed)

  1. [in Japanese] 高橋智栄, 千見寺浄慈, 時田恵一郎
    「タンパク質デザインの統計力学」
    日本物理学会誌 最近の研究から Vol 80, No. 3, pp.116-120, 2025
    DOI: 10.11316/butsuri.80.3_116 (2025年3月号).
  2. [in Japanese & in print only] 高橋智栄, 千見寺浄慈, 時田恵一郎
    「ベイズ学習による格子タンパク質模型のデザイン」
    学会誌「シミュレーション」, 最先端研究, Vol. 41, No.3, pp.30-35, 2022
    (2022年9月号).