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At present there are databases with over 500,000 predicted or synthesized MOF structures, yet a method to establish whether a new material adds new information does not exist. Here the authors propose a machine-learning based approach to quantify the structural and chemical diversity in common MOF databases.

The Co-catalysed hydroformylation of olefins produces selectively linear but not branched aldehydes. Here, the authors show that microporous MOFs increase the olefins density in the pores beyond neat conditions allowing high branched selectivity through kinetic modulation when added to a liquid phase hydroformylation mixture.