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arXiv · 2508.06156

Revealing the Staging Structural Evolution and Li (De)Intercalation Kinetics in Graphite Anodes via Machine Learning Potential

Liqi Wang·Xuhe Gong·Zicun Li·Ruijuan Xiao·Hong Li
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Abstract

Revealing the dynamic structural evolution and lithium transport properties during the charge/discharge processes is crucial for optimizing graphite anodes in lithium-ion batteries, enabling high stability and fast-charging performance. However, the dynamic coupling mechanisms among carbon layer kinetics, lithium (de)intercalation/diffusion, and defects regulation remain insufficiently understood. In this study, we developed a universal automated workflow based on machine learning potentials to simulate the dynamic lithium (de)intercalation process. With this approach, the staging structural evolution of lithium-graphite intercalation compounds and their lithium transport behavior were resolved through molecular dynamics simulations. By introducing stacking faults into the graphite structure, we successfully simulated stage transitions driven by carbon layer sliding and reorganization, accompanied by stress release and structural stabilization. The dynamics of carbon layers regulate the lithium (de)intercalation positional selectivity, producing intermediate states with varying lithium concentrations and distributions during cycling. This facilitates the formation and transformation of stage structures while mitigating residual stress accumulation. A fundamental kinetic asymmetry arises between lithium intercalation and deintercalation, driven by the continuous and heterogeneous lithium transport and carbon layer sliding during charge/discharge processes. The carbon defects regulate lithium transport, in which the atomic-scale defects confine intralayer lithium transport and carbon sliding while enabling interlayer transport via dynamic lithium trapping/release mechanisms. Accordingly, for the future design, it is critical to construct structural units with controllable carbon layer sliding/reorganization, and tunable defects to enhance lithium-ion transport.

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Liqi Wang, Xuhe Gong, Zicun Li, Ruijuan Xiao, Hong Li. 2025-08-08. Revealing the Staging Structural Evolution and Li (De)Intercalation Kinetics in Graphite Anodes via Machine Learning Potential. https://arxiv.org/abs/2508.06156

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