Machine learning (ML) provides a means to achieve these goals by identifying complex relationships between different features and a particular property. Recently Kim et al. utilized ML to identify critical chemical and structural features that affect ion mobility in anti-perovskite lattice-based solid-state batteries, highlighting properties such as hopping distance and channel width. In another investigation, López et al. utilized first-principles materials simulations and data analysis techniques to uncover that correlations between ion diffusivity and materials descriptors are most pronounced when linked to vibrational nature with anharmonic effects. Interestingly, elastic and vibrational descriptors, rather than conventional ones like chemical composition and ion mobility, prove to be more effective in categorizing solid-state electrolytes into universal groups. This highlights the significance of considering temperature effects in databases for an enhanced understanding and a generalized approach to energy material design. Optimizing these descriptors for ion mobility in energy storage materials is essential. A classification approach centered on the migration mechanism emerges as an appropriate strategy and by tailoring structural, electrochemical and transport properties, researchers can design materials with high ion mobility.