4 papers
Predicting highly correlated hydride-ion diffusion in SrTiO crystals based on the fragment kinetic Monte Carlo method with machine-learning potential
Hiroya Nakata
Oxyhydrides have drawn attention because of their fast ion conductivity and strong reducing properties. Recently, hydride ion migration in SrTiOH oxyhydride crystals…
Development of a fragment kinetic Monte Carlo method for efficient prediction of ionic diffusion in perovskite crystals
Hiroya Nakata
A massively parallel kinetic Monte Carlo (kMC) approach is proposed for simulating ionic migration in a crystal system by introducing the atomic fragmentation scheme (fragment kMC)…
Analytic First and Second Derivatives for the Fragment Molecular Orbital Method Combined with Molecular Mechanics
Hiroya Nakata, Dmitri G. Fedorov
Analytic first and second derivatives of the energy are developed for the fragment molecular orbital method interfaced with molecular mechanics in the electrostatic embedding schem…
Development of a New Parameter Optimization Scheme for a Reactive Force Field (ReaxFF) Based on a Machine Learning Approach
Hiroya Nakata, Shandan Bai
Reactive molecular dynamics (MD) simulation is performed using a reactive force field (ReaxFF). To this end, we developed a new method to optimize the ReaxFF parameters based on a…