We showed that a gradient boosting regressor (GBR) could be coupled to a set of ad hoc geometric descriptors, combining local features (Si-O distances, Si-O-Si angles, etc), global features (framework density, atomic volume, ring sizes) and porosity information (surface are, pore volume, pore size); see Figure . We found that the accuracy of this predictor, even when trained on a limited amount of DFT data (121 zeolites), was better than that of classical force fields for the prediction of bulk and shear moduli. In the later work of Gaillac et al., we pushed this approach to a new level by combining modelling at multiple levels of theory with supervised machine learning. The application in that case was the discovery of novel zeolitic structures with auxetic behavior (i.e., the presence of negative Poisson's ratio). Because that behavior is rare, to find the proverbial needle in the haystack, it was not possible to train a ML predictor on a randomly chosen subset of zeolites, but we needed to incorporate more materials with potential auxeticity.