Ren Yao, Zhang Kena, Zhou Yue, Cao Ye
Department of Materials Science and Engineering, University of Texas at Arlington, Arlington, Texas 76019, United States.
Department of Electrical Engineering and Computer Science, South Dakota State University, Brookings, South Dakota 57007, United States.
ACS Appl Mater Interfaces. 2022 Jul 13;14(27):30658-30671. doi: 10.1021/acsami.2c03000. Epub 2022 Jun 27.
Lithium (Li) dendrite growth in Li batteries is a long-standing problem, which causes critical safety concerns and severely limits the advancement of rechargeable Li batteries. Replacing a conventional liquid electrolyte with a solid electrolyte of high mechanical strength and rigidity has become a potential approach to inhibiting the Li dendrite growth. However, there still lacks an accurate understanding of the role of the mechanical properties of the metal electrode and the solid electrolyte in the Li dendrite growth. In this work, we develop a phase-field model coupled with the elastoplastic deformation to investigate the Li dendrite growth and its inhibition in the cell. Different mechanical properties, including the elastic modulus and the initial yield strength of both the metal electrode and the solid electrolyte, are explored to understand their independent roles in the inhibition of Li dendrite growth. High-throughput phase-field simulations are performed to establish a database of relationships between the aforementioned mechanical properties and the Li dendrite morphology, based on which a compressed-sensing machine learning model is trained to derive interpretable analytical correlations between the key material parameters and the dendrite morphology, as described by the dendrite length and area ratio. It is revealed that the Li dendrite can be effectively inhibited by electrolytes of high elastic moduli and initial yield strengths. Meanwhile, the role of the yield strength of the Li metal is also critical when the yield strength of the electrolyte becomes low. This work provides a fundamental understanding of the dendrite inhibition by mechanical suppression and demonstrates a computational data-driven methodology to potentially guide the electrode and electrolyte material selection for better inhibition of the dendrite growth.
锂电池中锂（Li）枝晶的生长是一个长期存在的问题，它引发了严重的安全担忧，并严重限制了可充电锂电池的发展。用具有高机械强度和刚性的固体电解质取代传统的液体电解质已成为抑制锂枝晶生长的一种潜在方法。然而，对于金属电极和固体电解质的机械性能在锂枝晶生长中的作用仍缺乏准确的认识。在这项工作中，我们开发了一个与弹塑性变形耦合的相场模型，以研究电池中的锂枝晶生长及其抑制情况。探索了包括金属电极和固体电解质的弹性模量和初始屈服强度在内的不同机械性能，以了解它们在抑制锂枝晶生长中的独立作用。进行了高通量相场模拟，以建立上述机械性能与锂枝晶形态之间的关系数据库，并在此基础上训练了一个压缩感知机器学习模型，以得出关键材料参数与枝晶形态之间可解释的分析相关性，如枝晶长度和面积比所描述的那样。结果表明，具有高弹性模量和初始屈服强度的电解质可以有效地抑制锂枝晶。同时，当电解质的屈服强度较低时，锂金属的屈服强度的作用也至关重要。这项工作为通过机械抑制来抑制枝晶生长提供了基本的理解，并展示了一种计算数据驱动的方法，有可能指导电极和电解质材料的选择，以更好地抑制枝晶生长。
