Nature of the Superionic Phase Transition of Lithium Nitride from Machine Learning Force Fields

Gabriel Krenzer, Johan Klarbing, Kasper Tolborg, Hugo Rossignol, Andrew McCluskey, Benjamin Morgan, Aron Walsh

Research output: Contribution to journalArticlepeer-review

3 Citations (SciVal)

Abstract

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-Li 3N. We characterized Li 3N 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.

Original languageEnglish
Pages (from-to)6133-6140
Number of pages8
JournalChemistry of Materials
Volume35
Issue number15
Early online date19 Jul 2023
DOIs
Publication statusPublished - 8 Aug 2023

Bibliographical note

Funding -
The Faraday Institution - FIRG025
Schmidt Futures Swedish Research Council (VR) 2021-00486
Irish Research Council RCLA/ 2019/127
Royal Society - UF130329
Royal Society - URF/R/191006
EPSRC (EP/P020194/1)

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