Nature of the Superionic Phase Transition of Lithium Nitride from Machine Learning Force Fields
Gabriel Krenzer, Johan Klarbring, Kasper Tolborg, Hugo Rossignol, Andrew R. McCluskey, Benjamin J. Morgan, Aron Walsh
Abstract
High Resolution Image Download MS PowerPoint Slide 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 3 N. We characterized Li 3 N 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.