Descriptors were calculated for a total of 590 448 zeolite frameworks from the PCOD2 database and 195 zeolite frameworks from the IZA. Our GBR model was trained using the DFT training set with hyperparameters described previously. The predicted results for K and G give distributions illustrated in Figure , plotted against framework density. While the GBR algorithm is unable to extrapolate we do not find this to be a limitation for this dataset with predictions of few zeolites exhibiting moduli similar to the bounds of the training structures. The large-scale simulation of the PCOD2 database clearly demonstrates a lack of correlation between K, G and density in the full space of hypothetical zeolites. Notably, most dense polymorphs show significantly less stiffness than those of low density. This relationship highlights the diverse mechanical response that can be achieved by the arrangement of tetrahedra. Furthermore, the lower K and G displayed by high-density zeolites may suggest a mechanical advantage to the formation of low density and microporous frameworks by SiO 2 .