In later work, we used this newly created database of thermal expansion coefficients at the DFT level to train machine learning models, in a workflow summarized on gradient-boosting regressor algorithm, depending on the type of descriptors used to encode the geometrical features of the zeolites. In particular, we compared ad hoc geometrical features, topology, pore space, to the performance of general geometric descriptors based on smooth overlap of atomic position (SOAP), which are not specific to zeolites. We showed that generic geometric descriptors performed best, and demonstrated that the predictions were not very sensitive to small changes in the input structure, i.e., that the outcome of the prediction did not necessarily require the refinement of framework structures at a high level of theory. This finding is very important for the generalizability of the technique, showing that the ML predictor obtained can be used on lower-quality structures. We then proceeded to apply the ML predictor to the PCOD2 database of zeolites containing around 600,000 hypothetical structures. This large-scale screening study confirmed a general statistical correlation between the thermal expansion coefficient and bulk modulus of the materials, with the Si-O-Si angles playing a key role in both.