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