We first develop descriptive models using cycling protocol parameters alone as inputs. These "protocol models" predict a single cell-level performance metric, such as an electrode-specific capacity or a Interpretable Data-Driven Modeling Reveals Complexity of Battery Aging trajectory descriptor at EOL conditions. Fig. schematically depicts the structure of these models, with the cycling protocol conditions as the input features and the EOL mechanistic SOH metrics as the target outputs. With only cycling conditions as input features, the EFC model attains good performance on the training/test set (SI Section S.9.5 for performance, and SI Section S.9.1 on train/test split). Wellperforming models are critical in order to extract the correct feature importance. To understand the impact of the various cycling conditions, we investigate feature importance using SHAP analysis . Fig. shows an example of SHAP feature importance for predicting EFC. In Fig. , we devise a 96 element aging matrix representation that comprehensively visualizes how cycling conditions affect each mechanistic SOH metric, where the color indicates the magnitude of feature importance for all cycling conditions. While no single cycling parameter dominates all mechanistic SOH metrics, it was surprising that many of the metrics are either primarily determined by a single cycling parameter, or a combination of features (e.g., the NP Ratio, a combination of Q PE and Q NE ) that are primarily influenced by a single cycling parameter . For example, the cell-level performance metrics Q RPT,1C and Q RPT,2C are dominated by CC 2 , the resistances R ohm and R ct are dominated by V charge , while R p is dominated CC 1 . Some more convoluted metrics, such as the EFC, depend on multiple parameters; both CC 1 and V discharge are about equally important. Surprisingly, V discharge and t CV do not dominate aging (within the bounds of this dataset) for most of the mechanistic SOH metrics (except for EFC), despite previous reports stating their importance .