4 papers
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…
AI4X Roadmap: Artificial Intelligence for the advancement of scientific pursuit and its future directions
Stephen G. Dale, Nikita Kazeev, Alastair J. A. Price +65
Artificial intelligence and machine learning are reshaping how we approach scientific discovery, not by replacing established methods but by extending what researchers can probe, p…
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…