Over the last 20 years, there has been a notable acceleration in the discovery of porous structures. This growth is illustrated in the timeline of Figure . The view of our field, once focused on experimental characterization of a select group of few commonly available materials, has since been strengthened by the use of simulation techniques for property prediction based on atomic structures, high-throughput computational screening studies, and the recent advances in artificial intelligence (AI) for faster materials discovery. Since it is standard (in for most journals even mandated) for new crystalline structures to be to published in a database, such as the CSD (Crystallography Structural Database), we now have access a large set of porous materials, including > 70, 000 crystalline MOFs. The large scale of such data sets is a novel dimension in the context of materials discovery: it can help accelerate the identification of existing materials for targeted applications through datadriven approaches, and serve as a basis for the prediction of novel hypothetical structures.