Artificial intelligence for carbon materials in electrochemical energy storage: Methods, applications, and perspectives

Abstract The transition to clean energy demands efficient and scalable electrochemical energy storage (EES) systems, including metal-ion and metal–sulfur batteries, as well as supercapacitors. Carbon materials are essential for EES owing to their exceptional electrical conductivity, electrochemical stability, and structural versatility, but their complex structures hinder structure–property mappings, thereby limiting traditional trial-and-error design. Artificial intelligence (AI) and machine learning (ML) offer transformative solutions by uncovering hidden structure–property relations and accelerating material discovery. Although various EES materials have been benefited from these advances, the study of carbon materials using this new paradigm is in its early stages. This review provides a comprehensive evaluation of AI-assisted methods applied to carbon materials across diverse EES systems, including metal-ion and metal–sulfur batteries, as well as supercapacitors. We systematically summarize core ML workflows in materials science, spanning data acquisition, feature engineering, and predictive modeling while highlighting AI-driven theoretical simulations, such as ML interatomic potentials. Finally, we outline emerging paradigms and prospects, emphasizing the integration of large language models, active learning to address data scarcity, and the development of autonomous self-driving laboratories for next-generation carbon electrode design. This review also presents challenges and limitations of applying AI-assisted methods to research on carbon materials for EES.