Small-data machine learning for resolving degradation challenges in energy devices

Abstract

Degradation is a central bottleneck for deploying next-generation energy devices, yet generating large, long-duration aging datasets is costly and slow. This Perspective surveys small-data machine learning (ML) techniques that extract maximal information from limited measurements to accelerate lifetime prediction, interpretation, and optimization. Using three case studies on batteries, fuel cells, and solar cells, we benchmark feature-engineered regression models and show that simple models using carefully chosen physics-based features can accurately forecast even with small datasets. We then demonstrate how interpretable ML links processing and operating parameters to degradation pathways and how physics-informed features improve model robustness. For optimization under data constraints, we compare Bayesian optimization (BO) and reinforcement learning, highlighting BO as a broadly applicable strategy across composition, manufacturing, and device operation optimization. We further describe data fusion and transfer learning strategies that combine multi-fidelity and multi-laboratory datasets and transfer knowledge across chemistries to mitigate data scarcity. Finally, we outline open challenges and research gaps in data, modeling, as well as hardware and software integration, aiming to motivate continued progress toward data-driven solutions for degradation challenges in energy device research and development.