Finally, we quantify and rationalize the predictive power of explainable features in early cycles, and demonstrate the value of features extracted from the early diagnostic cycles for early prediction of the models were employed to correlate EOL mechanistic aging features to cycling parameters, we construct a "diagnostic-aided model" that uses both cycling parameters and early values of the mechanistic SOH metrics (specifically, the evolution between the 1st and 3rd diagnostic cycle, Fig. ) as inputs to our interpretable ML model. The inclusion of features from early diagnostic cycles differentiates between cells with the same cycling parameters, giving insight into cell-to-cell variability in fixed aging conditions.