number of cycles. From these data, measures such as coulombic efficiency (CE) can be calculated and correlated to more in-depth techniques such as electrochmical impedance spectroscopy (EIS) to fundamentally assess the underlying degradation mechanisms. Accurate measurement of CE does, however, require bespoke instrumentation and a considerable amount of time, i.e. cycling a battery for 1000 cycles at 1C/1D takes approximately 11 weeks. Reducing the required number of cycles by a factor of 10, while maintaining a high level of fidelity, is therefore of great interest . Machine Learning (ML) and Deep Learning (DL) can accelerate testing by lowering the number of cycles required to understand the underlying chemistries . An example of predicting EOL of batteries using initial discharge capacity curves was demonstrated by Severson et al. , who used regression models.