Jiao Junyu, Lai Genming, Zhao Liang, Lu Jiaze, Li Qidong, Xu Xianqi, Jiang Yao, He Yan-Bing, Ouyang Chuying, Pan Feng, Li Hong, Zheng Jiaxin
School of Advanced Materials, Peking University, Shenzhen Graduate School, Shenzhen, 518055, P. R. China.
Shenzhen All-Solid-State Lithium Battery Electrolyte Engineering Research Center, Institute of Materials Research (IMR), Tsinghua Shenzhen International Graduate School, Shenzhen, 518055, P. R. China.
Adv Sci (Weinh). 2022 Apr;9(12):e2105574. doi: 10.1002/advs.202105574. Epub 2022 Feb 25.
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.
锂是用于最先进的二次电池的理想负极材料。然而，锂枝晶生长是一个安全问题，并且会导致库仑效率低下，这严重限制了锂二次电池的商业应用。不幸的是，在原子尺度上对锂沉积（生长）机制的了解甚少。在此，利用机器学习构建了具有量子力学计算精度的锂势模型。本研究中使用该模型进行的分子动力学模拟揭示了大型锂金属系统中的两种自愈机制，即表面自愈和体相自愈。得出的结论是，自愈在纳米尺度上迅速发生；因此，使用几种综合方法最小化锂晶粒之间的空隙可以有效地促进无枝晶锂的形成。
