Our framework exemplifies the value of SHAP as a tool for identifying correlations between input features and the target mechanistic SOH metrics. While the ML method alone does not differentiate between the contributions from two highly correlated electrodes, the explainable features together with scientific knowledge helps to hypothesize causation. Although we choose in this section to highlight and analyze low SOC resistance as one example, we emphasize that the approach generalizes to any mechanistic aging feature of interest (SI Section S.8.3).