锂(药物)
自愈
阳极
材料科学
枝晶(数学)
纳米技术
法拉第效率
机制(生物学)
金属锂
金属
纳米尺度
沉积(地质)
化学物理
复合材料
化学
冶金
物理
物理化学
地质学
替代医学
内分泌学
古生物学
病理
几何学
医学
量子力学
数学
电极
沉积物
作者
Junyu Jiao,Genming Lai,Lang Zhao,Jiaze Lu,Qidong Li,Xianqi Xu,Yao Jiang,Yan‐Bing He,Chuying Ouyang,Feng Pan,Hong Li,Jiaxin Zheng
标识
DOI:10.1002/advs.202105574
摘要
Li is an ideal anode material for use in state-of-the-art secondary batteries. However, Li-dendrite growth is a safety concern and results in low coulombic efficiency, which significantly restricts the commercial application of Li secondary batteries. Unfortunately, the Li-deposition (growth) mechanism is poorly understood on the atomic scale. Here, machine learning is used to construct a Li potential model with quantum-mechanical computational accuracy. Molecular dynamics simulations in this study with this model reveal two self-healing mechanisms in a large Li-metal system, viz. surface self-healing, and bulk self-healing. It is concluded that self-healing occurs rapidly in nanoscale; thus, minimizing the voids between the Li grains using several comprehensive methods can effectively facilitate the formation of dendrite-free Li.
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