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Machine learning predicts mechanical properties of porous materials

Dr Peyman Moghadam from our Department of Chemical and Biological Engineering has used machine learning techniques to accurately predict the mechanical properties of metal organic frameworks. These could be used to extract water from the air in the desert, store dangerous gases or power hydrogen-based cars. The results have been published in the inaugural edition of the Cell Press journal, Matter.