The GBR model, using these simple descriptors, provides excellent predictive performance without the need for complex descriptors, or the use of explicit molecular simulation at the classical or quantum level. A preliminary study indicates, however, that this cannot be directly extended to predict the anisotropy of elastic properties, or directional properties such as linear compressibility and Poisson's ratio. There, we expect that novel and more intricate descriptors may be required, including some global measure of the framework topology, connectivity, and directionality. In addition, we also investigated the possibility of using energy-based descriptors in the machine learning model, such as the energy per SiO 2 unit relative to α-quartz. We find that the addition of this new descriptor to the model does not improve its accuracy in K and G in a statistically significant way. This means that relative energy (or enthalpy of formation) and elastic properties are not linked. It also means, on a more practical level, that by excluding energy from our model we can still allow large-scale prediction of elastic moduli while using only local, structural and porosity features which are significantly cheaper to calculate than energy.