Predicting highly correlated hydride-ion diffusion in SrTiO₃ crystals based on the fragment kinetic Monte Carlo method with machine-learning potential

2021/05/02 by Hiroya Nakata, Nakata, Hiroya
Chemistry · Materials Science · Physics and Astronomy · #Advancements in Solid Oxide Fuel Cells #Chemical Physics (physics.chem-ph) #Chemical physics #Chemistry #Diffusion #Diffusion barrier #Electrode #FOS: Physical sciences #Hydride #Hydrogen #Inorganic chemistry #Ion #Ionic bonding #Ionic conductivity #Kinetic Monte Carlo #Materials Science (cond-mat.mtrl-sci) #Materials science #Monte Carlo method #Nanotechnology #Nuclear Materials and Properties #Organic chemistry #Oxygen #Physical chemistry #Physics #Thermodynamics #cond-mat.mtrl-sci #physics.chem-ph

paper · pdf · doi:10.48550/arxiv.2105.00414

published in arXiv (Cornell University) (Cornell University) · 18 page 7 figures

openalex publication_date 2021/05/02 · arxiv created 2021/06/13 · arxiv updated 2021/06/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Oxyhydrides have drawn attention because of their fast ion conductivity and strong reducing properties. Recently, hydride ion migration in SrTiO_3-xH_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.

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