One of the roadblocks on the way to a more systematic understanding is the limited availability of thermal expansion data in databases. In their recent study across all inorganic materials, Cai et al. extracted from the published literature 434 values -and not all of them measured or predicted in the same way, or at the same temperature. Systematic computational studies on the link between structure, chemical composition and thermal expansion have been published on smaller families of materials. Our group has devoted several studies to the understanding of physical properties of zeolitic frameworks, among which featured the thermal behavior. We have combined several approaches to allow for the prediction of this structure-property relationship, including both DFT calculations in the quasi-harmonic approximation (QHA), the training of a machine learning predictor based on DFT data, and more recently the development of a neural network-based interatomic potential for the description of allsilica zeolites, with the aim of reproducing their thermal expansion and mechanical response. However, such studies remain for now limited to small subsets of the chemical space.