Although the CNN and other deep learning models based on physics-inspired approaches present an avenue for quantitative structure-property linkage, understanding the relationship between the structural descriptors and the property of interest is difficult due to the "black box" nature of the deep learning models. In this section, we probe the influence of the structural descriptors on cation diffusivity using data-driven approaches, and we examine whether there are physical mechanisms underlying the optimal microstructures obtained using the combination of the CNN model and the topology optimization algorithm.