(107) By using the melting point as a target property for battery operational temperature windows, ES-LiR showed the most accurate estimation compared to multiple linear regression (MLR) and the least absolute shrinkage and selection operator (LASSO) approaches. Nevertheless, there are challenges in the application of DL to battery design. The efficacy and precision of DL models hinge heavily on the availability of high-quality and reliable data, which is often proprietary and not easily obtainable. Furthermore, these models can occasionally encounter the issue of overfitting, where they become excessively tailored to the training data, resulting in poor generalization and inaccurate predictions. This challenge is particularly pronounced in battery design, given the intricate interplay of various components, making it arduous to encompass all the pertinent variables within a single model. It is imperative to go beyond training-based machine learning and extrapolate insights from existing designs to new ones with limited data. Last but not least, there is a pressing need to expedite not only the research and development phase but also the scaling-up and manufacturing processes.