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Application of machine learning potentials to predict grain boundary properties in fcc elemental metals
Takayuki Nishiyama, Atsuto Seko, Isao Tanaka
Accurate interatomic potentials are in high demand for large-scale atomistic simulations of materials that are prohibitively expensive by density functional theory (DFT) calculatio…
Derivative structure enumeration using binary decision diagram
Kohei Shinohara, Atsuto Seko, Takashi Horiyama +3
A derivative structure is a nonequivalent substitutional atomic configuration derived from a given primitive cell. The enumeration of derivative structures plays an essential role…
Group-theoretical high-order rotational invariants for structural representations: Application to linearized machine learning interatomic potential
Atsuto Seko, Atsushi Togo, Isao Tanaka
Many rotational invariants for crystal structure representations have been used to describe the structure-property relationship by machine learning. The machine learning interatomi…