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
μm SSE film with high ionic conductivity and good uniformity
is used to construct a LiNi0.8Co0.1Mn0.1O2 || Li6PS5Cl || LiIn cell demonstrating
100 cycles. This study presents an efficient ML-assisted approach
to optimize scalable production of high-quality SSE films.