Integrating artificial intelligence with electrochemical science for advanced energy storage

Electrochemical systems play a critical role in modern technologies for energy storage, from portable devices to large-scale grid applications. However, discovery, optimization, and mechanistic understanding of electrochemical storage materials remain complex and resource-intensive. Advances in artificial intelligence (AI) and machine learning (ML) are now accelerating this progress, enabling smarter materials, faster screening, and deeper mechanistic insights. This review outlines how AI and ML are being integrated into electrochemical energy storage research, with emphasis on batteries, supercapacitors, and electrochemical sensors. Key advances include AI-guided discovery of electrode and electrolyte materials, predictive modeling for degradation and lifetime analysis, corrosion assessment, and workflow automation through autonomous electrochemical laboratories. In addition, related applications such as electrocatalyst design for hydrogen production and CO₂ conversion are discussed in the context of their potential for long-duration energy storage and renewable energy integration. Remaining challenges and future directions are identified, emphasizing the need for interdisciplinary collaboration. This review demonstrates how data-driven approaches are reshaping electrochemical science and accelerating innovation in sustainable energy storage technologies.