While comparing the predictive performance of models M (B) and M (P ) f on subsets of unseen D LN O and D N M C dataset (Supp. 15), M (P ) f demonstrates reliable predictive alignment for voltage drop, CE, and Q dis . In contrast, M (B), shows a divergent pattern in voltage drop predictions, which may be due to its training on data with inherently long relaxation time profiles compared to those in D LN O , where measurements are taken shortly after state changes. However, it maintains consistency in CE predictions and adjusts Q dis predictions in response to changes in the test protocol. The performance comparison for D N M C using these two models can be further explored in Supp. 21 and Supp. 23. relationship between models' predictions and the actual Q dis with the diagonal line representing perfect prediction accuracy, plots b) compare the density distribution of actual and predicted Q dis , plots c) present calibration curves that reflect the degree of alignment between predicted probabilities and observed frequencies under a normal distribution assumption. The discrete points on the calibration curve show the observed proportions of actual values that fall within three specific intervals based on the quantiles: between the 10th and 50th, 50th and 90th, and 10th and 90th percentiles. Model M (P ) f shows a high level of calibration for predicting Q dis of D LN O samples with a minimal miscalibrated area of 0.022. The points for 10th and 50th and 50th and 90th percentiles lie close to the diagonal line, indicating a nearly perfect calibration for these intervals. M (B) exhibits a slight overconfidence by deviating from the ideal line, with a miscalibration area of 0.16. The three calibration markers for M (B), are all positioned just below the diagonal line, showing uniform overconfidence across these quantile ranges, yet they remain close to this line, indicating a generally well-calibrated model. Plots d) show the prediction intervals across lifespan cycles, highlighting models' uncertainty over time and plots e) detail the distribution of prediction intervals' quantiles between the 10th and 90th percentiles, which convey the models' prediction uncertainty; a distribution skewed towards the lower quantiles suggests a higher confidence in predictions at these quantiles. The sharpness, as a measure of mean interval width, is approximately similar for both models at 3.7 × 10 -4 and 3.5 × 10 -4 for M (P ) f and M (B), respectively. Together, these plots demonstrate the M (P ) f 's precision in capturing discharge capacity behavior and M (B)'s robust generalization. In our analysis of D LN O for Q dis , Fig. demonstrates that M (P ) f achieves high predictive fidelity. This is evident from the dense alignment of the predictions with the actual values in the scatter plot (Fig. ), and the significant overlap in distributions seen in the density plot (Fig. ). The model's precision is further highlighted by concentrated prediction intervals and a calibration curve that closely traces the diagonal (Fig. ). It achieves a high proportion of data points within the predictive bounds, indicative of accuracy, without excessively wide intervals that could decrease the utility of the predictions. Panel II for M (B) also demonstrates a close tracking of the actual values, with a marginally broader prediction interval and higher miscalibrated area of 0.16 compared to M (P ) f 's of 0.022 (Panel I). Despite this variance, M (B) maintains a reasonable estimate range. Qualitatively (Table ), M (P ) f achieves better accuracy in predicting Q dis with a lower RMSE and MAE. M (B) Available Input Cycles Available Input Cycles The color intensity in these plots denotes the specific cycles from which the input parameter originates. Plots h), i), and j) show the sum of the logarithmic contribution of each input parameter towards predicting future cycles with a selective representation of three past cycle data. These visualizations confirm the model's attentive adjustment to the latest available input data and its capacity for generalization, despite the high experimental noise and limited battery performance.