3 papers
cond-mat.mtrl-sci2026
Global structure searches under varying temperatures and pressures using polynomial machine learning potentials: A case study on silicon
Hayato Wakai, Atsuto Seko, Isao Tanaka
Polynomial machine learning potentials (MLPs) based on polynomial rotational invariants have been systematically developed for various systems and applied to efficiently predict cr…
cond-mat.mtrl-sci2025
Systematic global structure search of bismuth-based binary systems under pressure using machine learning potentials
Hayato Wakai, Shintaro Ishiwata, Atsuto Seko
Machine learning potentials (MLPs) have significantly advanced global crystal structure prediction by enabling efficient and accurate property evaluations. In this study, global st…
cond-mat.mtrl-sci2024
Predictive power of polynomial machine learning potentials for liquid states in 22 elemental systems
Hayato Wakai, Atsuto Seko, Hirosato Izuta +2
The polynomial machine learning potentials (MLPs) described by polynomial rotational invariants have been systematically developed for various systems and used in diverse applicati…