113 citations · 486 across the 27 of their papers we have counts for
4 papers · 1 filter
Update of PHYSBO: Improving Usability and Portability of Bayesian Optimization for Physics and Materials Research
Yuichi Motoyama, Kazuyoshi Yoshimi, Tatsumi Aoyama +3
Bayesian optimization (BO) is widely used to accelerate physics and materials research, where objective function evaluations are computationally or experimentally expensive. While…
MateriApps LIVE! and MateriApps Installer: Environment for starting and scaling up materials science simulations
Yuichi Motoyama, Kazuyoshi Yoshimi, Takeo Kato +1
In our current era, numerical simulations have become indispensable theoretical and experimental tools for use in daily research activities, particularly in the materials science f…
Bayesian optimization package: PHYSBO
Yuichi Motoyama, Ryo Tamura, Kazuyoshi Yoshimi +3
PHYSBO (optimization tools for PHYSics based on Bayesian Optimization) is a Python library for fast and scalable Bayesian optimization. It has been developed mainly for application…
Facilitating {\it ab initio} configurational sampling of multicomponent solids using an on-lattice neural network model and active learning
Shusuke Kasamatsu, Yuichi Motoyama, Kazuyoshi Yoshimi +3
We propose a scheme for {\it ab initio} configurational sampling in multicomponent crystalline solids using Behler-Parinello type neural network potentials (NNPs) in an unconventio…