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. A. Seko, Machine learning potential repository

   [arXiv:2007.14206]
2. A. Seko, Machine learning potentials for multicomponent systems: The Ti-Al binary system

   Phys. Rev. B 102, 174104 (2020) [arXiv:2008.09750]
3. T. Nishiyama, A. Seko, and I. Tanaka, Application of machine learning potentials to predict grain boundary properties in fcc elemental metals

   Phys. Rev. Materials 4, 123607 (2020) [arXiv:2007.15944]
4. A. Seko, A. Togo and I. Tanaka, Group-theoretical high-order rotational invariants for structural representations: Application to linearized machine learning interatomic potential

   Phys. Rev. B 99, 214108 (2019) [arXiv:1901.02118]
5. A. Takahashi, A. Seko and I. Tanaka, Linearized machine-learning interatomic potentials for non-magnetic elemental metals: Limitation of pairwise descriptors and trend of predictive power

   J. Chem. Phys. 148, 234106 (2018). [arXiv:1710.05677]
6. A. Takahashi, A. Seko and I. Tanaka, Conceptual and practical bases for the high accuracy of machine learning interatomic potentials: Application to elemental titanium

   Phys. Rev. Materials 1, 063801 (2017). [arXiv:1708.02741]
7. A. Seko, A. Takahashi and I. Tanaka, First-principles interatomic potentials for ten elemental metals via compressed sensing

   Phys. Rev. B 92, 054113 (2015).
8. A. Seko, A. Takahashi and I. Tanaka, Sparse representation for a potential energy surface

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