Machine learning predicts mechanical properties of porous materials -- Department of Chemical Engineering and Biotechnology

Researchers from our Adsorption and Advanced Materials Group have used machine learning techniques to accurately predict the mechanical properties of metal organic frameworks (MOFs), materials which could be used to extract water from the air in the desert, store dangerous gases or power hydrogen-based cars. The researchers used their algorithm to predict the properties of more than 3000 existing MOFs, as well as MOFs which are yet to be synthesised in the laboratory. The results, published in the inaugural edition of the Cell Press journal Matter, could be used to significantly speed up the way materials are characterised and designed at the molecular scale. MOFs are self-assembling 3D compounds made of metallic and organic atoms connected together. Like plastics, they are highly versatile, and can be customised into millions of different combinations.