• Corpus ID: 233481927

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

@inproceedings{Nakata2021PredictingHC,
  title={Predicting highly correlated hydride-ion diffusion in SrTiO\$\_3\$ crystals based on the fragment kinetic Monte Carlo method with machine-learning potential},
  author={Hiroyasu Nakata},
  year={2021}
}
  • H. Nakata
  • Published 2 May 2021
  • Materials Science
Oxyhydrides have drawn attention because of their fast ion conductivity and strong reducing properties. Recently, hydride ion migration in SrTiO3−xHx 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… 

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