474 citations · 2.3k across the 36 of their papers we have counts for
5 papers · 1 filter
Machine learning potentials for multicomponent systems: The Ti-Al binary system
Atsuto Seko
Machine learning potentials (MLPs) are becoming powerful tools for performing accurate atomistic simulations and crystal structure optimizations. An approach to developing MLPs emp…
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…
Machine Learning Potential Repository
Atsuto Seko
This paper introduces a machine learning potential repository that includes Pareto optimal machine learning potentials. It also shows the systematic development of accurate and fas…
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…
Prediction of perovskite-related structures in ACuO (A Ca, Sr, Ba, Sc, Y, La) using density functional theory and Bayesian optimization
Atsuto Seko, Shintaro Ishiwata
Oxygen vacancy ordering in perovskite-type transition-metal oxides plays an important role in the emergence of exotic electronic properties, as typified by superconducting cuprates…