Nature of the Superionic Phase Transition of Lithium Nitride from Machine Learning Force Fields
- 등재 SCIE, SCOPUS
- OA유형 All Open Access; Hybrid Gold Open Access
- 발행기관 American Chemical Society
- 발행년도 2023
- 총서유형 Journal
- URI http://www.dcollection.net/handler/ewha/000000211359
- 본문언어 영어
- Published As https://doi.org/10.1021/acs.chemmater.3c01271
초록/요약
Superionic conductors have great potential as solid-state electrolytes, but the physics of type-II superionic transitions remains elusive. In this study, we employed molecular dynamics simulations, using machine learning force fields, to investigate the type-II superionic phase transition in α-Li3N. We characterized Li3N above and below the superionic phase transition by calculating the heat capacity, Li+ ion self-diffusion coefficient, and Li defect concentrations as functions of temperature. Our findings indicate that both the Li+ self-diffusion coefficient and Li vacancy concentration follow distinct Arrhenius relationships in the normal and superionic regimes. The activation energies for self-diffusion and Li vacancy formation decrease by a similar proportion across the superionic phase transition. This result suggests that the superionic transition may be driven by a decrease in defect formation energetics rather than changes in Li transport mechanism. This insight may have implications for other type-II superionic materials. © 2023 The Authors. Published by American Chemical Society.
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