The current model is based on battery test results from the Arbin Cycler. Multiple features were initially extracted from the time-series measurements. Based on Pearson correlation results (Supplementary Figure ), four features with the highest correlation coefficients were selected for input into our ML algorithm: end voltage (final voltage), median current, min dV/dt (minimum value of the time derivative of voltage measurements), and total current rise (sum of positive ∆I, where ∆I is the difference between the two neighboring current measurements). Similar to the velocity models, the charge saving score was the target variable, and six different ML models (linear regression, decision tree, random forest, extremely randomized trees, k-nearest neighbors, and Gaussian process) were implemented to predict it. Supplementary Figure shows the comparative predictive results from the six ML models. Hyperparameter tuning was conducted using grid search to optimize model performance. The selected parameters were then used to train each model 10 times, and the final result was determined by averaging the 10 runs to ensure the accuracy and generalizability of the conclusions.