We observed that M (P ), trained on public data, offers broader generalization across various battery types and protocols, albeit with increased uncertainty. M (B), trained on a more extensive dataset, demonstrates a lower uncertainty. This further motivates the importance of data sharing and management. Our findings also reveal that fine-tuning the models with few labels significantly improves their generalization to new chemistries, especially for M (B). The methodology outlined in this paper presents an opportunity for other researchers to create their own high-performance models. By retraining or fine-tuning with different datasets, researchers can tailor these predictive models to their specific experimental setups and desired outcomes. This flexibility allows for the exploration of different perspectives and approaches, facilitating the development of more accurate and specialized models.