<div>MOFs and COFs are porous materials with a large variety of applications including gas</div><div>storage and separation. Synthesised in a modular fashion from distinct building blocks, a</div><div>near in?nite number of structures can be constructed and the properties of the material can</div><div>be tailored for a speci?c application. While this modularity is a very attractive feature it also</div><div>poses a challenge. Attempting to identify the best performing material(s) for a given appli-</div><div>cation is experimentally intractable. Current research e?orts combine molecular simulations</div><div>and machine learning techniques to evaluate the simulated performance of hundreds of thou-</div><div>sands of materials to identify top performing MOFs and COFs for a given application. These</div><div>approaches typically rely on moderated brute-force screening which is still resource-intensive</div><div>as typically between 70 - 100 % of the hundreds of thousands of materials must be simulated</div><div>to create a training set for the machine learning models used, restricting screening to rela-</div><div>tively simple molecules. In this work we demonstrate our novel Bayesian mining approach</div><div>to materials screening which allows 62 - 92 % of the top 100 porous materials for a range of</div><div>applications to be readily identi?ed from large materials databases after only assessing less</div><div>than one percent of all materials. This is a stark contrast to the 0 - 1 % achieved by conven-</div><div>tional brute-force screening where porous materials are just chosen at random during a high</div><div>throughput screening. Through this accelerated virtual screening process, the identi?cation of</div><div>high performing materials can be used to more rapidly inform experimental e?orts and hence</div><div>lead to an acceleration of the entire research and development pipeline of porous materials.</div>