Since SHAP analysis cannot differentiate between correlated input features, in order to draw robust conclusions about the importance of early cycle features, it is necessary to also consider a "diagnosticonly" model, excluding cycling parameters as input features (SI Section S.9.4). In principle, this may affect the relative feature importance of the early cycle features which correlate with specific cycling parameters. In addition, this type of model may be preferred in cases where you either do not directly have access to cycling conditions, cycling conditions are kept constant, or the relationship to cycling conditions is not the focus . In this case, the exclusion of cycling conditions does not meaningfully affect the ranking of the feature importances (see SI Section S.9.4 for further details).