Random forest regression was chosen as the machine learning model of choice for all models in this work due to its ability in capturing non-linear relations with input features. We first generate a train/test split of the data, and cross validation folds on the training split. The cells that go in to the different splits are chosen randomly for the explanatory model, but an inside-of-domain testing scenario for the protocol only, diagnostic-only, and diagnostic-aided models (see SI Section S.9.1 for details). Random forest hyper parameters are optimized via grid search cross validation. From the subsequent trained model we report the RAE metric to accurately compare the prediction performance on different mechanistic SOH metrics of different scales and distributions. To determine feature importances we then use the SHAP python library on the fitted model to extract SHAP values for all features and datapoints (see SI Section S.9.5 for full parity plots and SHAP analysis). To summarize this information, we then take the absolute mean feature importance and report this value in the matrix plots.