Machine learning predicts heat capacities of MOFs

Metal organic frameworks capturing CO2 from flue gasses (Credit: S.M. Moosavi) - Chemical engineers at EPFL have developed a machine-learning model that can accurately predict the heat capacity of the versatile metal-organic framework materials. The work shows that the overall energy costs of carbon-capture processes could be much lower than expected. Metal-organic frameworks (MOFs) are a class of materials that contain nano-sized pores. These pores give MOFs record-breaking internal surface areas, which make them extremely versatile for a number of applications: separating petrochemicals and gases , mimicking DNA , producing hydrogen , and removing heavy metals , fluoride anions , and even gold from water are just a few examples. MOFs are the focus of Professor Berend Smit's research at EPFL School of Basic Sciences, where his group employs machine learning to make breakthroughs in the discovery, design, and even categorization of the ever-increasing MOFs that currently flood chemical databases. In a new study, Smit and his colleagues have developed a machine-learning model that predicts the heat capacity of MOFs. "This is about very classical thermodynamics," says Smit.