Microstructural Insights into Fast Ion Transport in Solid Electrolytes via Multiscale Modeling

Abstract

Improving solid electrolytes is critical for high-performance all-solid-state batteries, yet the microstructural features that enable fast ion transport remain poorly understood. Here, we use multiscale modeling to resolve polycrystalline ion transport from atomic-scale hopping at grain boundaries to continuum-scale percolation, thereby providing insights into realistic solid-electrolyte microstructures. Accurate lightweight machine-learning potentials -- developed via closed-loop active learning for exemplar argyrodites Li$_6$PS$_5$X, X $\in$ {Cl, Br, I} -- are employed to integrate molecular dynamics with finite element simulations. We find that diffusion barriers of the anion-ordered bulk scale linearly with anion radius. Grain boundaries exert opposite effects depending on the bulk: enhancing ion diffusion in low-diffusivity phases but suppressing it in fast-diffusing ones. Li$_6$PS$_5$I exhibits non-Arrhenius transport behavior consistent with experimental observations. Our results clarify the pivotal role of grain boundaries in ion transport and guide a priori microstructural design of advanced solid electrolytes.