Optimizing rechargeable batteries is a challenging and necessary task as energy storage is deployed to decarbonize transportation and the electricity grid. The battery design space is large, spanning chemistry, architecture, manufacturing processes, and usage conditions. Besides, battery lifetime is long and its evaluation is time-and resource-consuming, generally lasting several months to years, even under accelerated testing conditions. Over the past few decades, there have been substantial efforts to shorten battery lifetime evaluation, such as developing accelerated testing protocols (typically under elevated temperatures) and electrochemical methods (such as highprecision coulometry) . Physics-based models and semi-empirical models have also been widely used to capture battery degradation trajectories. More recently, substantial progress has been made in data-driven approaches for battery lifetime prediction, typically involving machine learning (ML) methods on large datasets .