Unraveling defect-mediated ion transport behavior in antiperovskite solid-state electrolyte via machine learning molecular dynamics simulations

Abstract

Anti-perovskite (AP) solid-state electrolytes (SSE) have emerged as promising candidates for high-safety solid-state batteries due to their wide electrochemical window and good compatibility with lithium metal. However, their relatively low ionic conductivity significantly hinders their commercial application. Although defect engineering is considered a key strategy to enhance ionic conductivity, the influence of different defect types on the microscopic mechanisms of ion diffusion remains unclear. Moreover, conventional simulation methods struggle to accurately capture the temperature-dependent ion diffusion behavior in complex defect systems. Herein, we employed machine learning molecular dynamics simulations to systematically investigate the effects of various vacancy, interstitial, and composite defects on lithium ion transport in AP Li3OCl SSE. Simulation results demonstrated that the type of defect significantly influences lithium ion diffusion ability. The lithium ion diffusion ability of the defective systems decreases in the following order: systems with Li vacancies > systems with Li interstitial defects > systems with only anion vacancies > perfect crystal structure. Notably, non-Arrhenius behavior was observed in some defective systems. Structural analysis revealed that the non-Arrhenius behavior originates from thermally disordered-induced local octahedral distortions and the correspondingly generated high-energy lithium-ion sites.This study provides a significant micro-level theoretical foundation for understanding the mechanisms governing ion transport in AP materials.