arXiv subjects

Hiroya Nakata

Publications and source records attributed to Hiroya Nakata.

6 recordsLinked to original sources

arXiv2026

Orbital-Free DFT-Assisted Machine-Learned Molecular Dynamics for Electric-Field-Driven Ionic Transport

We propose an orbital-free density functional theory (OFDFT)-assisted machine-learned molecular dynamics method in which field-independent interatomic forces are evaluated using a machine-learned potential and atomic charges that depend on the local environment are obtained from OFDFT calculations. The atomic charges obtained by Bader partitioning of the OFDFT electron density are multiplied by the electric-field vector and added to the forces from the machine-learned potential. This approach enables molecular dynamics simulations under an electric field at a lower computational cost than Kohn-Sham DFT (KSDFT)-based molecular dynamics. As a proof of concept, the method was applied to beta-Li3PS4 bulk and an S8/Li3PS4 heterostructure under periodic boundary conditions. For Li3PS4 bulk, OFDFT yielded Li charges and a total charge of the PS4 unit consistent with those obtained using KSDFT. In the heterostructure, Li ions migrated from the Li3PS4 region into the S-rich region within a simulation time of approximately 170 ps. Accompanying this migration, the mean Bader charge of the S atoms that initially formed S8 rings changed from nearly neutral to negative. A comparison with KSDFT for a representative interfacial structure also confirmed that the negative charging of the S atoms upon the arrival of Li and the magnitude of the Li charges were qualitatively reproduced. These results demonstrate the possibility of treating field-driven ionic transport and changes in interfacial charge states using a universal machine-learned potential without training an additional material-specific charge-prediction model.

cond-mat.mtrl-sci↗

arXiv2021

Investigation of the effect of surface phosphate ester dispersant on viscosity by coarse-grain modeling of BaTiO$_3$ slurry

To understand the role of phosphate ester dispersant, we investigated the rheology of a BaTiO$_3$ slurry. For the model case, a coarse-grain molecular dynamics (CGMD) simulation was performed with the butyral polymer didodecyl hydrogen phosphate (DHP), in the toluene/ethanol solvent. By systematically analyzing the effect of DHP from an atomic-scale first principle and from all-atom MD to micro-scale CGMD simulation, we investigated how the adsorption of a DHP dispersant on a BaTiO$_3$ surface affects the microstructure rheology of a BaTiO$_3$ slurry. The first-principle and all-atom MD simulation suggests that DHP molecules prefer to locate near the BaTiO$_3$ surface. CGMD simulation shows a reduction in viscosity with an increase in dispersants, suggesting that the dispersant population near the BaTiO$_3$ surface plays a key role in controlling the rheology of the BaTiO$_3$ slurry. In this study, we propose an approach for understanding the BaTiO$_3$ slurry with molecular-level simulations, which would be a useful tool for efficient optimization of slurry preparation.

cond-mat.mtrl-sci↗

arXiv2021

Predicting highly correlated hydride-ion diffusion in SrTiO$_3$ crystals based on the fragment kinetic Monte Carlo method with machine-learning potential

Oxyhydrides have drawn attention because of their fast ion conductivity and strong reducing properties. Recently, hydride ion migration in SrTiO$_{3-x}$H$_{x}$ oxyhydride crystals has been investigated, showing that hydride ion migration is blocked by slow oxygen diffusion. In this study, we investigate the hydride-ion migration mechanism using a kinetic Monte Carlo approach to understanding the relationship between the hydride and oxygen ions. The difficulties in applying the method to hydride and oxygen ion migration involve complex changes in the ionic migration barrier, which shifts dynamically depending on the characteristics of the surrounding hydride and oxygen ions. We can predict these complex changes using a machine-learning neural network model. The simulation can then be performed using this model to predict the temperature-dependent ionic-migration behavior. We found that our simulation results with respect to the activation barrier for hydride ion diffusion accorded well with those obtained by experiment. We also found that hydride ion migration is affected by slow oxygen diffusion and that oxygen diffusion is accelerated by changes in the ionic migration barriers. The parallel-processing efficiency of our proposed method was 84.92 \% for our 1,000-CPU implementation, suggesting that the approach should be widely applicable to simulations of ionic migration in crystals at a reasonable computational cost.

cond-mat.mtrl-sci↗

arXiv2020

Development of a fragment kinetic Monte Carlo method for efficient prediction of ionic diffusion in perovskite crystals

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). The fragment kMC method achieved a reasonable parallel efficiency with 1728 central processing unit (CPU) cores, and the method enables the simulation of ionic diffusion in $μ$m-scale perovskite crystals. To demonstrate the feasibility of the proposed approach, the fragment kMC method was applied to predict the diffusion coefficients of hydrogen and oxygen in SrTiO$_{(3-x)}$H$_x$ and BaTiO$_{(3-x)}$H$_x$ system. Finally, the fragment kMC method was customized for $μ$-scale BaTiO$_3$ simulation under an applied bias voltage, and oxygen diffusion in BaTiO$_3$ model was evaluated. The respective grain sizes are sub-nanometre, and we conclude that the proposed fragment kMC method can be applied to calculate the extent of ionic migration in $μ$-scale materials with fully atomistic simulation models at a reasonable computational cost.

physics.chem-ph↗

arXiv2020

Analytic First and Second Derivatives for the Fragment Molecular Orbital Method Combined with Molecular Mechanics

Analytic first and second derivatives of the energy are developed for the fragment molecular orbital method interfaced with molecular mechanics in the electrostatic embedding scheme at the level of Hartree-Fock and density functional theory. The importance of the orbital response terms is demonstrated. The role of the electrostatic embedding upon molecular vibrations is analyzed, comparing force field and quantum-mechanical treatments for an ionic liquid and a solvated protein. The method is applied for 100 protein conformations sampled in MD to take into account the complexity of a flexible protein structure in solution, and a good agreement to experimental data is obtained: frequencies from an experimental IR spectrum are reproduced within 17 cm$^{-1}$.

physics.chem-ph↗

arXiv2018

Development of a New Parameter Optimization Scheme for a Reactive Force Field (ReaxFF) Based on a Machine Learning Approach

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 machine learning approach. This approach combines the $k$-nearest neighbor and random forest regressor algorithm to efficiently locate several possible ReaxFF parameter sets, thereby the optimized ReaxFF parameter can predict physical properties even in a high-temperature condition within a small effort of parameter refinement. As a pilot test of the developed approach, the optimized ReaxFF parameter set was applied to perform chemical vapor deposition (CVD) of an $α$-Al$_2$O$_3$ crystal. The crystal structure of $α$-Al$_2$O$_3$ was reasonably reproduced even at a relatively high temperature (2000 K). The reactive MD simulation suggests that the (11$\overline{2}$0) surface grows faster than the (0001) surface, indicating that the developed parameter optimization technique could be used for understanding the chemical reaction in the CVD process.

physics.chem-ph↗