From Trial‐and‐Error to Artificial Intelligence‐Guided Design of Hard Carbon for Sodium‐Ion Batteries

Sodium‐ion batteries (SIBs) are regarded as a promising complement to lithium‐ion batteries for large‐scale energy storage, with hard carbon standing out as one of the most promising anode materials. However, establishing a clear structure–performance relationship between its intricate microstructure and electrochemical behavior remains a significant challenge. Although numerous important feedbacks governing sodium storage in hard carbon anodes have been obtained through orthogonal experimental studies, the intrinsic structural complexity of hard carbon materials means that the current understanding of their sodium storage mechanisms remains incomplete. Recent advances in artificial intelligence (AI) and machine learning (ML) offer powerful data‐driven strategies to systematically decode and quantify structure–property correlations, enabling more efficient design and optimization of hard carbon anodes. This review summarizes the evolution of traditional methodologies and highlights representative applications of ML in exploring the structure–performance relationships of hard carbon anodes. It also discusses how AI‐driven approaches can overcome the limitations of transcending conventional research and development models. Finally, forward‐looking discussion on current challenges and future directions, aiming to offer new perspectives for advancing the practical application of SIBs, is provided.