Algorithm accurately predicts mechanical properties of existing and theoretical MOFs

A machine learning algorithm that can predict the mechanical properties of metal–organic frameworks (MOFs) offers a way to overcome these highly varied and versatile materials' achilles heel – their instability.1 The team behind this work hope that this computational tool will speed up acceptance of these materials by industry. MOFs are a type of crystalline coordination polymers that form porous structures by combining metal clusters and organic ligands. 'Their "building block" nature allows chemists to easily tune their syntheses to tailor the pore size and surface chemistry for a specific application,' explains David Fairén-Jiménez at the University of Cambridge, UK. 'However, if you wish to use MOFs in real life, you need to shape them into pellets, and this densification may destroy their porosity, thus their functionality.'