Enhancing composite cathode manufacturing with machine learning for polymer electrolyte solid-state batteries

Solid-state batteries (SSBs) represent a pivotal advancement in battery technology, poised to surpass lithium-ion batteries and drive the electrification of mobility. However, achieving cost-effective, scalable, and sustainable fabrication processes for SSB components remains a challenge. This study integrates machine learning (ML) techniques to optimize manufacturing processes of cathodes for polymer electrolyte based SSBs. The findings reveal strong predictive performance of regression models for active material loading, with support vector machine emerging as the top performer. Multiclassification models exhibit satisfactory precision, particularly in categorizing ideal electrode samples. Analysis of principal component and correlation circles highlight viscosity and wet thickness as critical variables for mixing and coating, respectively. Despite promising metrics, dataset imbalance and size limit model robustness. Further dataset augmentation is recommended before deployment in production. ML techniques offer promise in advancing battery manufacturing, paving the way for enhanced SSB performance and broader application across battery components. [Display omitted] •Machine learning optimized cathode manufacturing for solid-state batteries.•Models achieved high predictive accuracy for active material loading.•Viscosity and wet thickness were key to improving electrode fabrication.•Key variables improved mixing and coating processes in electrode production.