Machine Learning-Assisted Microstructural Quantification of Multiphase Cathode Composites in All-Solid-State Batteries: Correlation with Battery Performance

Abstract

Microstructure optimization and high-performance material development are crucial for improving the electrochemical performance of all-solid-state batteries (ASSBs). Researchers frequently record numerous micro-scale or nano-scale electron micrographs for unbiased post-mortem analysis, performance evaluation, and improvement of ASSBs. However, these micrographs are often underutilized and typically analyzed qualitatively without ensuring an accurate representation of the experimental objectives. This study explores machine learning (ML)-based quantitative analysis techniques using electron microscopy images, combined with a stereology-driven linear-intercept concept method and semantic segmentation, to extract quantitative microstructural parameters for optimizing ASSB performance. The applicability of ML-assisted image analytics is demonstrated by employing composite cathodes in ASSBs to achieve unbiased automation and deep semantic segmentation during microstructural characterization. Furthermore, the ramifications of this ML-assisted method are discussed, along with its advantages and disadvantages in battery research.