Synchrotron Imaging of Li Metal Anodes in Solid State Batteries Aided by Machine Learning

Reversible lithium metal anodes that can achieve high rate capabilities are necessary for next generation energy storage systems. Solid electrolyte can act as a barrier for unwanted physical and chemical decomposition that lead to unstable electrodeposition (e.g. dendrite and filament growth). The formation and growth of filaments is tied to unique chemo-mechanical properties that exists at buried solid|solid interfaces. Herein, in situ tomography of Li|LLZO|Li cells is carried out to track morphological transformations in Li metal electrodes and buried solid|solid interfaces during stripping and plating processes. Optimized experimental parameters provide high resolution, high contrast reconstructions that enable lithium metal visualization. Machine learning and image processing tools are combined to quantify changes in lithium metal during stripping and plating. The analysis enables quantifying local current densities and pore size distribution in lithium metal during cycling experiments. Hotspots in lithium metal are correlated with microstructural anisotropy in the solid electrolyte. Modeling studies show large heterogeneity in transport and mechanical properties of electrolyte at the electrode|electrolyte interfaces. Regions with lower effective properties (transport and mechanical) are nuclei for failure. Failure is attributed to microstructural heterogeneities in the solid electrolyte that lead to high local stress and flux distributions.