Design of multicomponent argyrodite based on a mixed oxidation state as promising solid-state electrolyte using moment tensor potentials

All-solid-state batteries composed of inorganic solid-state electrolytes (SSEs) have received significant attention as candidates for next-generation batteries due to their enhanced safety and high energy density compared to conventional lithium-ion batteries (LIBs) that use organic liquid electrolytes. Despite their advantages, SSEs are not widely used in LIBs due to their low ionic conductivity and electrochemical instability. Much effort has been made to improve SSE performance by using sulfide-based argyrodite to optimize the composition of the multicomponent structure. However, due to the large number of viable compositions in multicomponent argyrodite systems, finding the best chemical combination of multiple components has proven challenging. In this study, we focused on mixed oxidation states in multicomponent argyrodite (Li 5+2 x + y [A] x 4+ [B] y 5+ [C] 1− x − y 6+ S 5 [D]) systems to systematically explore the effect of mixed oxidation states and develop machine-learned moment tensor potentials (MTPs) to evaluate ionic conductivity through large-scale simulations for high-performance SSE screening. Our results reveal that SSE performance has the order of [A] 4+ - [C] 6+ > [A] 4+ - [B] 5+ - [C] 6+ > [A] 4+ - [B] 5+ > [B] 5+ - [C] 6+ in mixed oxidation states. We found the [A] 4+ - [C] 6+ combination resulted in the best performance, with significant improvements in ionic conductivity and electrochemical stability, including interfacial and air stability, due to the synergy of the solid-electrolyte effect of [C] 6+ and the dynamic lattice effect of [A] 4+ . This work demonstrates that the argyrodite material class can be optimized through mixed oxidation states, shedding light on ways to systematically explore and design high-performance multicomponent argyrodite-based SSEs. Design of multicomponent argyrodite based on the mixed oxidation state as promising solid-state electrolytes using moment tensor potentials.