The purpose of this perspective is to assess the importance of the feature classes for lifetime predictions. Thus, we need features which are derived equivalently across charging and discharging curves (e.g., using the same feature extraction routine). For this reason, we employ features developed by Greenbank and Howey . Briefly, these features quantify the fraction of time a given time series of interest (voltage, current, etc.) spends in a given window (e.g., 3.5V to 3.6V) during a given time interval (10-cycle window in our study). See SI for a complete explanation of the featurization. For simplicity, models using features based on the charging data, the discharging data, or the full cycling data are referred to as "charge" (Class 1), "discharge" (Class 2) or "full" (Class 1) model, respectively. Finally, we derive the features at different points in the early cycles of the cells. This allows us to study how the information carried by such features evolves during the early aging of the cells.