Feedback, physics, and forecasts: The emerging paradigm of machine learning-driven battery research

Abstract Machine learning (ML) is reshaping how we understand, predict, and optimize electrochemical systems. In batteries, ML accelerates discovery across chemistry, design, and operation by transforming massive experimental and simulated datasets into predictive, interpretable models. This review consolidates a decade of progress in ML-driven battery innovation, from early-cycle feature extraction to operando image analysis and physics-informed modeling. We categorize approaches by data domain and physical fidelity, emphasizing interpretable ML for diagnostics, reinforcement learning for control, and multi-objective optimization for lifetime extension strategies. Additionally, we demonstrate how integrated models accelerate discovery, reduce testing time, and guide sustainable design. Economic analyses furthermore illustrate how these advances can lower cost per cycle and improve circularity. Together, these developments chart a path toward self-optimizing, sustainable battery technologies. Graphical abstract Conceptual overview of machine learning-driven battery research, illustrating how physics-based constraints, feedback-driven optimization, and predictive forecasting jointly enable adaptive and robust battery systems. The figure was generated with the assistance of generative AI tools (Gemini and ChatGPT) and curated by the authors.