1 citations · 1 across the 4 of their papers we have counts for
4 papers
Weisfeiler-Lehman subtree encoding for Bayesian optimization of atomic configurations
Akira Kusaba, Tatoshi Yonemori, Tetsuji Kuboyama +1
The efficiency of Bayesian optimization (BO) of atomic configurations depends strongly on how configurations are encoded. We introduce the Weisfeiler-Lehman (WL) subtree kernel, wh…
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…
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,…
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…