By varying six cycling parameters across this dataset (SI Table ), we induce a diverse range of EOL states and trajectory descriptors (Fig. ). To understand the impact of cycling conditions on mechanistic SOH metrics, we construct nonlinear random forest ML models, and then employ Shapley additive explanations (SHAP) analysis to interpret these models.