Supervised learning of materials properties of molecules and solid materials has been demonstrated to afford machine learning (ML) models which enable property predictions of new materials with low approximation error. Equally important, their computational cost is negligible when compared to conventional quantum methods, such as density functional theory (DFT). Over the first phase of MARVEL, von Lilienfeld and Ceriotti have made substantial progress on the development and application of unsupervised and supervised ML models. The findings have enabled the systematic and rigorous classification of materials, the identification and ranking of low-dimensional governing collective variables, as well as reaching an unprecedented combination of computational speed and predictive power across compositional and configurational compound space. The latter aspect subsequently enabled the discovery of ~90 crystals of the Elpasolite structure, predicted to be thermodynamically stable.