Fabrication of High-Quality Thin Solid-State Electrolyte Films Assisted by Machine Learning

Solid-state electrolytes (SSEs) are promising candidates to circumvent flammability concerns of liquid electrolytes. However, enhancing energy densities by thinning SSE layers and enabling scalable coating processes remain challenging. While previous studies have addressed thin and flexible SSEs, mainly ionic conductivity was considered for performance evaluation, and no systematic research on the effects of manufacturing conditions on the quality of SSE films was performed. Here, both uniformity and ionic conductivity are considered for evaluating the SSE films under the guidance of machine learning (ML). Three algorithms, principal component analysis, K-means clustering, and support vector machine, are employed to decipher the interdependencies between manufacturing conditions and film performance. Guided by ML, a 40 mu m SSE film with high ionic conductivity and good uniformity is used to construct a LiNi0.8Co0.1Mn0.1O2 parallel to Li6PS5Cl parallel to LiIn cell demonstrating 100 cycles. This study presents an efficient ML-assisted approach to optimize scalable production of high-quality SSE films.

Automated summary

This study utilizes machine learning to evaluate solid-state electrolyte (SSE) films based on uniformity and ionic conductivity, presenting an efficient approach to optimize scalable production of high-quality SSE films.