Developing a model that generalizes well necessitates a diverse and large dataset that ideally covers a spectrum of chemistries and formats given high-dimensional correlations and cell variations , obtained from various laboratories and measured under different operating conditions 7 . Data diversity not only ensures an accurate representation of different cycling behaviors, but also tames the irreducible uncertainty in the predictions while mitigating the risk of overfitting. However, the scarcity of large and comprehensive datasets 36 that include both high and low-performing cells creates a challenge for training generalized models, i.e., to overcome a positive bias . Available data often exhibit noise, discontinuities, and varying formats that require extensive curation, adding a layer of complexity.