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

TL;DR
This study uses machine learning potentials to simulate and analyze the structural evolution and lithium transport mechanisms in graphite anodes, revealing insights into phase transitions, defect roles, and kinetic asymmetries during battery cycling.
Contribution
The paper introduces a universal machine learning-based workflow for simulating lithium intercalation in graphite, capturing dynamic structural changes and defect effects with high accuracy.
Findings
Simulated staging structural evolution during lithium intercalation.
Identified the role of stacking faults in phase transitions.
Discovered kinetic asymmetry between lithium intercalation and deintercalation.
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,…
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Taxonomy
TopicsAdvancements in Battery Materials · Recycling and Waste Management Techniques · Advanced Battery Materials and Technologies
