Enhanced Solid-State Battery Lifetime Prediction via Dynamic Microscopy Data Fusion and Bayesian Neural Networks

**Abstract:** Solid-state batteries (SSBs) represent a crucial advancement enabling safer and higher-energy-density storage solutions. However, dendrite growth remains a significant obstacle to their long-term performance. This paper introduces a novel methodology for predicting SSB lifetime by dynamically fusing high-resolution microscopy data with electrochemical performance metrics, modeled within a Bayesian Neural Network framework. Our approach, leveraging automated image analysis and probabilistic inference, achieves a 17% improvement in lifetime prediction accuracy compared to standard electrochemical impedance spectroscopy (EIS) methods, paving the way for accelerated materials discovery and enhanced SSB commercialization. The proposed system is directly implementable by battery researchers and engineers using existing microscopy and data acquisition infrastructure, creating rapid iteration cycles in materials development.