材料科学
阳极
电化学
原子单位
相(物质)
化学物理
无定形固体
硅
压力(语言学)
结构稳定性
纳米技术
电极
结晶学
物理化学
冶金
化学
工程类
哲学
物理
结构工程
量子力学
有机化学
语言学
作者
Fangjia Fu,Xiaoxu Wang,Linfeng Zhang,Yifang Yang,Jianhui Chen,Bo Xu,Chuying Ouyang,Shenzhen Xu,Fu‐Zhi Dai,E Weinan
标识
DOI:10.1002/adfm.202303936
摘要
Abstract Unraveling the reaction paths and structural evolutions during charging/discharging processes are critical for the development and tailoring of silicon anodes for high‐capacity batteries. However, a mechanistic understanding is still lacking due to the complex phase transformations between crystalline (c‐) and amorphous (a‐) phases involved in electrochemical cycles. In this study, by employing a newly developed machine learning potential, the key experimental phenomena not only reproduce, including voltage curves and structural evolution pathways, but also provide atomic scale mechanisms associated with these phenomena. The voltage plateaus of both the c‐Si and a‐Si lithiation processes are predicted with the plateau value difference close to experimental measurements, revealing the two‐phase reaction mechanism and reaction path differences. The observed voltage hysteresis between lithiation and delithiation mainly originates from the transformation between the c‐Li 15‐ δ Si 4 and a‐Li 15‐ δ Si 4 phases. Furthermore, stress accumulation is simulated along different reaction paths, indicating a better cycling stability of the a‐Si anode due to the lower stress concentration. Overall, the study provides a theoretical understanding of the thermodynamics of the complex structural evolutions in Si anodes during (de)lithiation processes, which may play a role in optimizations for battery performances.
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