3 papers
physics.comp-ph2020
Machine-learning-based sampling method for exploring local energy minima of interstitial species in a crystal
Kazuaki Toyoura, Kansei Kanayama
An efficient machine-learning-based method combined with a conventional local optimization technique has been proposed for exploring local energy minima of interstitial species in…
cond-mat.mtrl-sci2020
A Sampling Strategy in Efficient Potential Energy Surface Mapping for Predicting Atomic Diffusivity in Crystals by Machine Learning
Kazuaki Toyoura, Takeo Fujii, Kenta Kanamori +1
We propose a machine-learning-based (ML-based) method for efficiently predicting atomic diffusivity in crystals, in which the potential energy surface (PES) of a diffusion carrier…
stat.ML2019
Active learning for enumerating local minima based on Gaussian process derivatives
Yu Inatsu, Daisuke Sugita, Kazuaki Toyoura +1
We study active learning (AL) based on Gaussian Processes (GPs) for efficiently enumerating all of the local minimum solutions of a black-box function. This problem is challenging…