20 citations · 21 across the 5 of their papers we have counts for
5 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…
Tiling decomposition multiplicity predicts stability of GaN(0001) surface reconstructions
Tetsuji Kuboyama, Akira Kusaba, Karol Kawka +1
The stable adatom configurations of a semiconductor surface have traditionally been sought by sampling: density functional theory (DFT) energies steer a heuristic or Bayesian searc…
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…
Exploration of a new reconstructed structure on GaN(0001) surface by Bayesian optimization
Akira Kusaba, Yoshihiro Kangawa, Tetsuji Kuboyama +1
GaN(0001) surfaces with Ga- and H-adsorbates are fundamental stages for epitaxial growth of semiconductor thin films. We explore stable surface structures with nanometer scale by t…