Next, we compare ML performance using "Class 1" and "Class 2" features (Figure ) as a function of the number of early cycles used as inputs. The errors of the "charge" and "full" models are equivalent, and substantially better than the "discharge" model. Importantly, the "charge" models work equally well using only features from cycle 1 vs. features from cycle 150. The low prediction errors of these models are explained by the fact that the "charge" features encode both the protocol-to-protocol and the cell-to-cell variability. This makes the "charge" models even more accurate than the "Class 0" model. In contrast, the "discharge" models are blinded from cycling information. Thus, their predictive power is limited if only the first tens of cycles are employed for the featurization, as shown in Figure . However, their accuracy improves steadily with increasing early aging data, approaching the other two models' accuracy after over 100 cycles, consistent with Severson et al. . Additionally, SI Figure demonstrates that all models' errors are approximately equivalent if cycling information is manually included into the features. These results confirm that a model not blinded from features that encode the intentionally varied cycling conditions will have an artificially inflated early prediction accuracy. On the contrary, predicting lifetime without knowledge of the cycling conditions is a considerably more challenging task.