Solid-state lithium metal batteries (LMB) consist of a new solution to store energy delivering lighter, longer ranges, and more powerful energy batteries. Different from traditional lithium-ion, LMB uses solid electrodes and electrolytes to provide superior electrochemical performance and high energy density. Some of the challenges of this new technology are to predict the cycling stability and to prevent the formation of Li dendrite growth. This phenomenon may occur during LMB charge and discharge, when Li can deposit irregularly, building up dendrites (Li plating) that leads to failures, such as short-circuit. These morphologies are key to the LMB quality, and they can be captured and analyzed using X-ray tomography (XRT) scans. This project delivers a new set of machine learning algorithms, focused on XRT data about LMB, to quantify LMB defects, as well as new protocols to monitor the lifespan of a LMB and the evolution of them during cycling.