16 citations · 18 across the 4 of their papers we have counts for
5 papers
Spin Neural Network Potential for Magnetic Phase Transitions in Uranium Dioxide
Keita Kobayashi, Hiroki Nakamura, Mitsuhiro Itakura
Uranium dioxide (UO2) is a prototypical nuclear fuel material, yet predicting its thermophysical properties across a wide temperature range remains challenging. One factor contribu…
Specific Heat Anomalies and Local Symmetry Breaking in (Anti-)Fluorite Materials: A Machine Learning Molecular Dynamics Study
Keita Kobayashi, Hiroki Nakamura, Masahiko Okumura +2
Understanding the high-temperature properties of materials with (anti-)fluorite structures is crucial for their application in nuclear reactors. In this study, we employ machine le…
Self-learning path integral hybrid Monte Carlo with mixed ab initio and machine learning potentials for modeling nuclear quantum effects in water
Bo Thomsen, Yuki Nagai, Keita Kobayashi +2
The introduction of machine learned potentials (MLPs) has greatly expanded the space available for studying Nuclear Quantum Effects computationally with ab initio path integral (PI…
Self-learning hybrid Monte Carlo method for isothermal-isobaric ensemble: Application to liquid silica
Keita Kobayashi, Yuki Nagai, Mitsuhiro Itakura +1
Self-learning hybrid Monte Carlo (SLHMC) is a first-principles simulation that allows for exact ensemble generation on potential energy surfaces based on density functional theory.…
Self-learning Hybrid Monte Carlo: A First-principles Approach
Yuki Nagai, Masahiro Okumura, Keita Kobayashi +1
We propose a novel approach called Self-Learning Hybrid Monte Carlo (SLHMC) which is a general method to make use of machine learning potentials to accelerate the statistical sampl…