Nature of the Superionic Phase Transition of Lithium Nitride from Machine Learning Force Fields Article Swipe
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· 2023
· Open Access
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· DOI: https://doi.org/10.1021/acs.chemmater.3c01271
Superionic conductors have great potential as solid-stateelectrolytes,but the physics of type-II superionic transitions remains elusive.In this study, we employed molecular dynamics simulations, using machinelearning force fields, to investigate the type-II superionic phasetransition in & alpha;-Li3N. We characterized Li3N above and below the superionic phase transition by calculatingthe heat capacity, Li+ ion self-diffusion coefficient,and Li defect concentrations as functions of temperature. Our findingsindicate that both the Li+ self-diffusion coefficient andLi vacancy concentration follow distinct Arrhenius relationships inthe normal and superionic regimes. The activation energies for self-diffusionand Li vacancy formation decrease by a similar proportion across thesuperionic phase transition. This result suggests that the superionictransition may be driven by a decrease in defect formation energeticsrather than changes in Li transport mechanism. This insight may haveimplications for other type-II superionic materials.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1021/acs.chemmater.3c01271
- OA Status
- hybrid
- Cited By
- 13
- References
- 67
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4384825749