First, we parametrized the discharge profiles. We processed data from a single aging cycle representative of the long-term cycling experiments using time-series current data only. We extracted a total of 11 parameters that could be related to real vehicle operation such as the average C-rate, the current variance (normalized), or the charge-discharge ratio. Additionally, a Fourier transform of the current signal was performed to recover the key peak frequencies in each discharge protocol. Section S.2.6.1 provides more details about this featurization procedure and Table lists all features generated. For the robustness and repeatability of our ML pipeline, we removed correlated features as discussed in section S.2.6.2.