2 papers
cond-mat.mtrl-sci2026
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…
cond-mat.mtrl-sci2025
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,…