GenAI for Scientific Discovery in Electrochemical Energy Storage: State‐of‐the‐Art and Perspectives from Nano‐ and Micro‐Scale

Abstract The transition to electric vehicles (EVs) and the increased reliance on renewable energy sources necessitate significant advancements in electrochemical energy storage systems. Fuel cells, lithium‐ion batteries, and flow batteries play a key role in enhancing the efficiency and sustainability of energy usage in transportation and storage. Despite their potential, these technologies face limitations such as high costs, material scarcity, and efficiency challenges. This research introduces a novel integration of Generative AI (GenAI) within electrochemical energy storage systems to address these issues. By leveraging advanced GenAI techniques like Generative Adversarial Networks, autoencoders, diffusion and flow‐based models, and multimodal large language models, this paper demonstrates significant improvements in material discovery, battery design, performance prediction, and lifecycle management across different types of electrochemical storage systems. The research further emphasizes the importance of nano‐ and micro‐scale interactions, providing detailed insights into optimizing these interactions for improved efficiency and longevity. Additionally, the paper discusses the challenges and future directions for integrating GenAI in energy storage research, highlighting the importance of data quality, model transparency, workflow integration, scalability, and ethical considerations. By addressing these aspects, this research sets a new benchmark for the use of GenAI in battery development, promoting sustainable, efficient, and safer energy solutions.