第一原理計算と機械学習による高精度原子間ポテンシャルの開発

  1. Group-theoretical high-order rotational invariants for structural representations: Application to linearized machine learning interatomic potential

    A. Seko, A. Togo and I. Tanaka,

    [arXiv:1901.02118]

  2. Linearized machine-learning interatomic potentials for non-magnetic elemental metals: Limitation of pairwise descriptors and trend of predictive power

    A. Takahashi, A. Seko and I. Tanaka,

    J. Chem. Phys. 148, 234106 (2018). [arXiv:1710.05677]

  3. Conceptual and practical bases for the high accuracy of machine learning interatomic potentials: Application to elemental titanium

    A. Takahashi, A. Seko and I. Tanaka,

    Phys. Rev. Materials 1, 063801 (2017). [arXiv:1708.02741]

  4. First-principles interatomic potentials for ten elemental metals via compressed sensing

    A. Seko, A. Takahashi and I. Tanaka,

    Phys. Rev. B 92, 054113 (2015).

  5. Sparse representation for a potential energy surface

    A. Seko, A. Takahashi and I. Tanaka,

    Phys. Rev. B 90, 024101 (2014). [arXiv:1403.7995]