63 citations · 115 across the 11 of their papers we have counts for
11 papers
A Metadynamics-Based Framework for Free Energy Surface Mapping of Multiparticle Diffusion in Crystals
Shunya Yamada, Kazuaki Toyoura
We propose two metadynamics (MetaD)-based methodologies for efficiently mapping free energy surfaces (FESs) of multiple interacting carriers diffusing in crystalline solids. Our ap…
Metadynamics for Vacancy Dynamics in Crystals
Kazuaki Toyoura, Shunya Yamada
We propose a metadynamics-based (MetaD-based) approach to construct the free energy surface (FES) for vacancy dynamics in crystals. In this approach, the vacancy FES can be constru…
Nuclear Quantum Effects on Proton Diffusivity in Perovskite Oxides
Shunya Yamada, Kansei Kanayama, Kazuaki Toyoura
In the present study, the nuclear quantum effects (NQEs) on proton diffusivity in oxides were evaluated by molecular dynamics (MD) simulations with the quantum thermal bath (QTB) b…
Quantitative evaluation of nuclear quantum effects on the phase transitions in BaTiO3 using large-scale molecular dynamics simulations based on machine learning potentials
Kansei Kanayama, Kazuaki Toyoura
The machine learning potential (MLP) based molecular dynamics (MD) method was applied for constructing the pressure-temperature phase diagram in the barium titanate (BaTiO3) crysta…
First-principles study of phase transition in cadmium titanate by molecular dynamics incorporating nuclear quantum effects
Kansei Kanayama, Kazuaki Toyoura
First-principles molecular dynamics (FPMD) simulations were applied for the paraelectric-ferroelectric phase transition in the perovskite-type cadmium titanate, CdTiO3. Since the p…
Multi-Hill Strategy in Metadynamics for Interstitial Diffusion in Crystals
Kazuaki Toyoura
We propose an efficient and general strategy of metadynamics (MetaD) for investigating interstitial diffusion in a crystal by exploiting crystallographic symmetry. Assuming complet…
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…
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…
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…
Exploring a potential energy surface by machine learning for characterizing atomic transport
Kenta Kanamori, Kazuaki Toyoura, Junya Honda +7
We propose a machine-learning method for evaluating the potential barrier governing atomic transport based on the preferential selection of dominant points for the atomic transport…
A machine learning-based selective sampling procedure for identifying the low energy region in a potential energy surface: a case study on proton conduction in oxides
Kazuaki Toyoura, Daisuke Hirano, Atsuto Seko +5
In this paper, we propose a selective sampling procedure to preferentially evaluate a potential energy surface (PES) in a part of the configuration space governing a physical prope…