Unveiling sodium storage mechanisms in hard carbon via machine learning-driven simulations integrated with accurate site occupation identification

-hybridized carbon bonds. For the first time, we simulate the continuous voltage profile associated with the stepwise sodium insertion during both the charging and overcharging states. This voltage profile reproduces experimental observations and disentangles the contributions of adsorption, intercalation, and pore filling, offering a pathway to elucidate the storage mechanisms across different systems and rationalize the discrepancies observed in experiments. During the overcharging stage, excessively short Na-Na distances enhance repulsion, leading to negative voltages. Besides, the formation of sodium clusters was observed, which pose a safety risk to the battery. Our findings demonstrate that machine learning-based simulations constitute a powerful and emerging approach for investigating sodium storage mechanisms and offer valuable guidance for the experimental optimization of HC anodes. Moreover, this strategy can be extended to other electrodes, electrolytes in SIBs, and even alternative battery systems.