To do so, we screened a database of nearly 600,000 hypothetical zeolitic structures at the classical level, through the use of a force field. Eliminating mechanically unstable structures, we could identify 578 frameworks predicted to be auxetic by the force field: we selected those for further study, and combined these with another 742 chosen at random. On this balanced subset of structures, we performed DFT calculations, obtaining a data set of 991 DFT-stable zeolitic frameworks. This was used as training set to optimize a machine learning predictor (again through the use of a gradient boosting regressor). This entire workflow, illustrated on Figure , shows a good example of the combination of molecular simulations at two different levels of theory to generate a representative data set for training a ML model. We expect such multi-scale modelling strategies to play an important role in the materials discovery pipeline, combined with experimental feedback and the use of optimal experimental design, or approaches such as multi-fidelity Bayesian optimization. More recently, some authors have proposed to use deep learning methods in order to predict elastic properties across the entire chemical space of crystals, a harder problem because it needs to take into account both geometrical and chemical considerations. Some authors have used this approach to predict scalar quantities, such as Mazhnik et al. who targeted hardness and fracture toughness in a study in order to identify new superhard materials.