Second, to determine electrode-specific capacities/SOCs, we implement a mechanistic model-fitting algorithm to extract seven interpretable quantities (see Methods for details): 1) negative electrode capacity: Q NE , 2) positive electrode capacity: Q PE , 3) lithium capacity: Q Li , 4) State of charge of the negative electrode near the full cell charged state: SOC NE,4.0V , 5) state of charge of the negative electrode in the discharged state: SOC NE,2.7V , 6) state of charge of the positive electrode near the charged state: SOC PE,4.0V , and 7) state of charge of the positive electrode in the discharged state: SOC PE,2.7V . We select the latter four quantities because the electrode-specific SOC near the fully discharged and fully charged states can dominate aging. These cell-level and electrode-specific metrics are calculated at every diagnostic cycle for each cell and tracked from beginning of life (BOL) to EOL. As would be expected for commercial cells, these metrics have low variability at BOL (Fig. ). Importantly, this low variability also confirms that the staggered start of cycling (resulting in different calendar aging in the discharged state) does not contribute Interpretable Data-Driven Modeling Reveals Complexity of Battery Aging significantly to the initial conditions of the cells (SI Section S.5). However, by EOL there is high variation in the rate capability, resistance, and electrode-specific capacities/SOCs (Fig. , and SI Fig. ). This observation underscores the importance of using a comprehensive set of SOH metrics, and confirms that the cycling conditions in this work induce a wide range of degradation trajectories.