Although the level of absolute uncertainty in Figure may seem high in some cases, it is useful to remind ourselves that the determination of elastic constants in porous materials by computational methods is a difficult challenge, and the values obtained are typically asso- gies, and mechanical properties, clearly illustrate the effectiveness of this machine learning approach. Similar predictions for the elastic moduli of pure-silica zeolites were reported by Siddorn and co-workers 3 using five different classical interatomic potentials: BKS, Catlow, Gale, Sastre, 40 and Matsui models. Figure displays the comparison between the bulk and shear moduli calculated by DFT methods and the results obtained with the five classical potentials. We can see that the spread of the plots is similar or worse than the predictions obtained by our machine learning approach. Moreover, we can also see some trends in the force fields themselves, with systematic errors: most force fields tend to systematically overestimate the bulk modulus, apart from the Matsui potential -which however systematically underestimates the shear modulus.