3 papers
cond-mat.mtrl-sci2026
Molecular dynamics with a first-principles-validated universal machine-learning potential reveals dynamic elementary processes of growth-related adspecies on GaN(0001)
Yoshito Takaesu, Akira Kusaba, Junko Ishii +2
Atomic-scale understanding of the surface elementary processes in metalorganic vapor phase epitaxy (MOVPE) of GaN has so far relied on static density-functional-theory (DFT) energe…
cond-mat.mtrl-sci2026
PyAPX: Python toolkit for atomic configuration pattern exploration
Akira Kusaba, Tetsuji Kuboyama, Karol Kawka +2
In materials discovery, the integration of first-principles calculations with machine learning techniques has been actively studied for two key tasks: crystal structure prediction,…
cond-mat.mtrl-sci2025
Exploration of stable atomic configurations in graphene-like BCN systems by Bayesian optimization
Taichi Hara, Akira Kusaba, Yoshihiro Kangawa +4
h-BCN is an intriguing material system where the bandgap varies considerably depending on the atomic configuration, even at a fixed composition. Exploring stable atomic configurati…