Both M (B) and M (P ) focused on predicting three parameters, which were selected for their established significance in the existing literature and their availability across the datasets. They included discharge capacity, crucial for understanding the SOH , CE, as emphasized in studies by Burns et al. as the key to understanding the impact of electrode additives and electrode materials on battery long-term performance, and the voltage drop during the relaxation phase between charging and discharging cycles. The last parameter is less explored but, as described by e.g. Zhu et al. , it offers valuable insights independent of the charging process. This parameter is easily calculated from cycling data, even if the studies where the data originated did not directly measure it. In this section, we evaluate our model's performance on various scenarios, focusing on the impact of data quality on model generalization and interpretability, investigating its adaptability to different chemistries, and deriving insights from attention mechanisms and saliency analysis.