In this study, we develop a holistic framework for revealing and explaining coupled battery aging pathways by combining interpretable ML, physically-derived mechanistic SOH metrics, and a diverse dataset spanning over 200 distinct cycling conditions. By tracking a comprehensive set of 16 mechanistic aging features, we fully describe the battery SOH through an aging matrix, provide insight into battery degradation mechanisms and also identify mechanistic features from early cycles that enable early predictions.