New model learns to predict heat capacity of MOFs

Metal-organic frameworks (MOFs) are a class of materials with lots of nano-sized pores. These pores give MOFs record-breaking internal surface areas, which make them extremely versatile for a number of applications. These include separating petrochemicals and gases, mimicking DNA, producing hydrogen, and removing heavy metals, fluoride anions and even gold from water.

MOFs are the focus of Berend Smit’s research at the EPFL School of Basic Sciences in Switzerland. Here, his group employs machine learning to make breakthroughs in the discovery, design and even categorization of the ever-increasing numbers of MOFs that currently flood chemical databases. In a paper in Nature Materials, Smit and his colleagues now report a novel machine-learning model that can predict the heat capacity of MOFs.

“This is about very classical thermodynamics,” says Smit. “How much energy is needed to heat up a material by one degree? Until now, all engineering calculations have assumed that all MOFs have the same heat capacity, for the simple reason that there is hardly any data available.”

“If there is no data, how can one make a machine-learning model? That looks impossible!” adds Seyed Mohamad Moosavi, a postdoc in Smit’s group. Nevertheless, Smit, Moosavi and their colleagues managed to develop a machine-learning model that predicts how the local chemical environment changes the vibrations of each atom in a MOF molecule.

“These vibrations can be related to the heat capacity,” says Smit. “Before, a very expensive quantum calculation would give us a single heat capacity for a single material, but now we get up to 200 data points on these vibrations. So, by doing 200 expensive calculations, we had 40,000 data points to train the model on how these vibrations depend on their chemical environment.”

The researchers then tested their model against experimental data. “The results were surprisingly poor,” says Smit, “until we realized that those experiments had been done with MOFs that had solvent in their pores. So, we re-synthesized some MOFs and carefully removed the synthesis solvent, measured their heat capacity, and the results were in very good agreement with our model’s predictions!”

“Our research showcases how artificial intelligence (AI) can accelerate solving multi-scale problems,” says Moosavi. “AI empowers us to think about our problems in a new way and even sometimes tackle them.”

To demonstrate the real-world impact of this work, engineers at Heriot-Watt University in the UK simulated the MOFs performance in a carbon-capture plant. “We used quantum molecular simulations, machine learning and chemical engineering in process simulations,” explains Smit. “The results showed that, with correct heat capacity values of MOFs, the overall energy cost of the carbon capture process can be much lower than we originally assumed. Our work is a true multi-scale effort, with a huge impact on the techno-economic viability of currently considered solutions to tackle climate change.”

This story is adapted from material from EPFL, with editorial changes made by Materials Today. The views expressed in this article do not necessarily represent those of Elsevier. Link to original source.