Through our interpretable ML framework, we deepen our physical intuition on battery degradation with a diverse dataset. While interpretable ML tools can be used to generate hypotheses and summaries of the dataset, the findings must be further validated with physical characterization to gain confidence. We urge the field to use the dataset presented here to expand upon this work while keeping interpretability in mind as to enrich our understanding of battery degradation.