In particular, data-driven approaches tend to overlook the inherent variations between, for example, production batches or individual cells . Such discrepancies, originating from manufacturing processes or aging mechanisms, can profoundly impact lifetime predictions. Addressing these variations for accurate forecasting remains a central yet unresolved research question. Furthermore, despite the assertions of recent studies that they are chemistry-agnostic , they often require enhanced explainability to optimize their effectiveness in various chemistry settings. Transfer learning offers a promising solution to the challenge of scarce data, but requires more investigation for transparency and interpretability . The acquisition of extensive datasets, essential for DL algorithms , remains a significant hurdle . Nevertheless, innovative strategies, such as the use of common features in databases and the documentation of various chemistries and protocols , establish the foundation for more in-depth research . Our goal is to develop a model that is not only universally applicable, but also robust, with the capability to provide both uncertainty quantification and explainability. Such a model would be invaluable to the academic community and would find marketable applications in the real world , accelerating battery design and data collection based on active learning.