In this study, we develop a model trained on ca. 17400 batteries from BASF research laboratories that covers a diverse range of LIBs chemistries and multiple cycling protocols. Exposure of our model to such a wide variety of data enables robust generalization. Utilizing our pre-trained model on a unique set of unseen data, we effectively predict the early degradation trajectory. The ultimate test of our model, therefore, is to apply it to data from cells produced in a different location and with varying chemistries. Due to intellectual property constraints that prevent the authors from making the model trained on the BASF dataset openly accessible, we have retrained our model by leveraging a diverse array of publicly available datasets from respected institutions and research groups, including the Toyota Research Institute (TRI) in partnership with MIT and Stanford 2, 1 , NASA , the Center for Advanced Life Cycle Engineering (CALCE) , Karlsruhe Institute of Technology (KIT) , Hawaii Natural Energy Institute (HNEI) , and Sandia National Laboratories (SNL) . Furthermore, we have incorporated data from our in-house cycled cells with successful and failed experiments, to further enrich model training and reduce bias. In the Supplementary Section 1 we provide an overview of all datasets; we include a brief summary in table 1 with an indication of which datasets were used during training and which remained completely unseen for model testing. This approach ensures a thorough understanding of the data sources, thus improving the transparency and reproducibility of our research.