Initially, they screened 12,831 crystalline solids with lithium content to identify structures possessing high structural and chemical stability, as well as low electronic conductivity and cost. Subsequently, they developed a data-driven model utilizing logistic regression to clas-sify ionic conductivity, based on experimental measurements reported in literature, thereby identifying candidate structures with fast lithium conduction. By employing this screening process, they narrowed down the list of candidates from 12,831 to 21 structures that exhibit promise as electrolytes, with only a few having undergone experimental examination. Interestingly, they found that individual atomistic descriptor functions lack predictive power for ionic conductivity; however, employing a multi-descriptor model proves to be useful. Furthermore, they observed that screening for structural stability, chemical stability, low electronic conductivity, and high ionic conductivity eliminates a significant portion of lithium-containing materials, reducing the list substantially. Notably, their screening approach capitalizes on structures and electronic information available in the Materials Project database. The examples presented in this chapter demonstrate that accelerated materials discovery methods are increasingly used to derive trends in ion mobility. However, it is still not clear how general the observed trends are with respect to different materials classes which will be discussed in the next section.