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

14 June 2023, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


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 characterised 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 change in defect formation behaviour, rather than changes in Li transport mechanism. This insight may hold implications for other type-II superionic materials.


superionic conductors
solid electrolytes
lattice dynamics
machine learning force fields
machine learning molecular dynamics


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