High-rate capable, reversible lithium metal anodes are necessary for next generation energy storage systems. In situ tomography of Li|LLZO|Li cells is carried out to track morphological transformations in Li metal electrodes. Machine learning enables tracking the lithium metal morphology during galvanostatic cycling. Nonuniform lithium electrode kinetics are observed at both electrodes during cycling. Hot spots in lithium metal are correlated with microstructural anisotropy in LLZO. Mesoscale modeling reveals that regions with lower effective properties (transport and mechanical) are nuclei for failure. Advanced visualization combined with electrochemistry represents an important pathway toward resolving non-equilibrium effects that limit rate capabilities of solid-state batteries.

中文翻译：

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机器学习辅助锂金属固态电池中孔形成的同步加速成像

高容量，可逆锂金属阳极是下一代储能系统所必需的。对Li | LLZO | Li电池进行原位层析以跟踪Li金属电极中的形貌转变。机器学习可在恒电流循环过程中跟踪锂金属的形态。在循环期间在两个电极上观察到不均匀的锂电极动力学。锂金属中的热点与LLZO中的微观结构各向异性相关。中尺度建模表明，具有较低有效属性（运输和机械）的区域是失效的核。先进的可视化技术与电化学技术相结合，是解决限制固态电池速率能力的非平衡效应的重要途径。