Artificial Intelligence-Assisted Dopant Discovery toward Air-Stable Sulfide Solid-State Electrolytes

Abstract

Sulfide solid-state electrolytes (SSEs) are highly promising candidates for all-solid-state batteries (ASSBs), but the severe degradation triggered by moisture exposure remains a major obstacle to their large-scale application. Despite compositional doping could effectively enhance the air stability, there is currently no relevant screening strategy to support the efficient exploration of dopants. Herein, we develop an artificial intelligence-assisted dopant screening platform for air-stable sulfide SSEs (LPSC). Retrieval-augmented system combined with large language model could effectively prescreen the dopants that enhance both the ionic conductivity, and interfacial compatibility of the electrolyte. More importantly, the impact of dopants on the air stability of electrolytes was quantified by the minimum Gibbs free energy change () of the hydrolysis reaction, which could be predicted by a composition-based machine learning model. Guided by this strategy, several promising dopants, including BiF3, CoF3, InF3, and SnF4 were successfully identified. As a proof of concept, LPSC-BiF3 electrolyte demonstrates superior air stability, delivering a high ionic conductivity retention of 91% after exposure to air with 10% RH for 6 h. Consequently, the exposed LPSC-BiF3 electrolyte enabled excellent cycling stability in Li-In||NCM811 full cells. The proposed screening strategy significantly promotes the exploration of high-performance dopants for air-stable sulfide SSEs.