AI-Driven Approaches for Navigating Battery Complexity

Abstract

Artificial intelligence (AI) is emerging as a powerful approach for navigating the complexity of battery materials and electrochemical systems. This mini-review highlights recent advances in AI-enabled battery research from a materials and electrochemistry perspective, with emphasis on electrolyte and electrode design. Key modeling frameworks, including physics-based, data-driven, and hybrid approaches, are discussed alongside representative machine-learning methods used to predict electrochemical performance, degradation behavior, and interfacial phenomena. Persistent challenges, including limited data quality, restricted model generalizability, and limited interpretability, remain significant barriers to widespread adoption. By outlining both opportunities and limitations, this review emphasizes the importance of integrating AI with physical insight and experimental validation to accelerate the development of safer, longer-lasting, and higher-performance energy storage technologies.