7 papers · 1 filter
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…
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…
Projector-based efficient estimation of force constants
Atsuto Seko, Atsushi Togo
Estimating force constants for crystal structures is crucial for calculating various phonon-related properties. However, this task becomes particularly challenging when dealing wit…
Globally-stable and metastable crystal structure enumeration using polynomial machine learning potentials in elemental As, Bi, Ga, In, La, P, Sb, Sn, and Te
Atsuto Seko
Machine learning potentials (MLPs) have become indispensable for conducting accurate large-scale atomistic simulations and for the efficient prediction of crystal structures. Polyn…
Polynomial machine learning potential and its application to global structure search in the ternary Cu-Ag-Au alloy
Atsuto Seko
Machine learning potentials (MLPs) have become indispensable for performing accurate large-scale atomistic simulations and predicting crystal structures. This study introduces the…
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…