AI-Driven Computational Insights into Electrochemical Energy Storage: A Review of Emerging Trends in Battery Chemistry

Abstract

Modern battery technologies depend on electrochemical storage systems. However, the performance of such systems is limited by complicated multi-scale processes. These processes include transport of ions, interfacial reactions, and degradation processes. These coupled physicochemical dynamics across scales cannot be captured accurately by traditional computational methods. This review analyzes more than 150 recently published studies (2018-2025) on the seamless integration of artificial intelligence like machine learning and deep learning and physics-informed hybrid models with standard computational chemistry frameworks. As materials discovery and performance optimization are increasingly accelerated by AI-powered methods, this review provides a systematic overview of their applications, successes, and challenges in electrochemical energy storage. AI-powered techniques are speeding the discovery of high-performing electrode materials (Ni-rich cathodes, solid-state electrolytes). AI can increase battery lifetime prediction accuracy by 30-50%. It can also lead to the development of digital twins for real-time monitoring and optimization of systems. Also, the review notes developing trends towards autonomous laboratories and self-optimizing battery systems, where AI connects data-driven insights to fundamental chemical understanding. Our study reveal the immense potential of AI in developing next-generation, sustainable, and circular electrochemical energy storage technologies. Further, they also highlight the challenges and research directions for effective deployment.