Our group originally proposed this approach on a subset of nanoporous materials with consistent chemistry, in order to probe directly the effect of geometry and topology on mechanical properties, without influence of the chemical composition of the materials. Puresilica zeolites offer a suitable playground for this kind of study, with a large number of known and hypothetical SiO 2 polymorphs: 237 experimentally known framework topologies, and more than 500,000 hypothetical structures. In a first study, we demonstrated with the work of Evans et al. that it was possible to predict average (polycrystalline) mechanical moduli such as the bulk modulus or shear modulus, through a machine learning approach.