Defect engineering for tailoring lithium-ion conduction in layered antiperovskite Li7O2Br3 solid-state electrolytes using a machine- learning potential

JJ Liu and RY Wang and YX Gao and ZC Zhong, JOURNAL OF ALLOYS AND COMPOUNDS, 1046, 184679 (2025).

DOI: 10.1016/j.jallcom.2025.184679

Solid electrolytes are pivotal for next-generation solid-state batteries, yet their ionic conductivity is fundamentally limited by defect-induced ion scattering. While traditional doping strategies exacerbate the trade-off between conductivity and ion scattering, inspired by semiconductor modulation doping techniques, we propose a decoupling approach to control charge defects in the antiperovskite material Li7O2Br3. By introducing Li-Br Schottky defects and optimizing Br vacancies spatially, we significantly mitigated scattering effects that obstruct Li+ transport. Deep-potential molecular-dynamics (DPMD) simulations with our trained DP model predict an 240 % boost in room- temperature ionic conductivity, while DP-based NEB calculations yield a Li-ion migration barrier of just 0.27 eV-the lowest value yet reported for layered antiperovskite solid electrolytes. These findings establish a novel defect engineering strategy to enhance the ionic conductivity of solid-state electrolytes, offering a pathway toward next-generation high-performance solid-state batteries.

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