Machine-Learning Analysis Could Help Reduce Carbon Emissions

In a novel approach that could help reduce carbon emissions, a team of scientists led by Stony Brook’s Anatoly Frenkel have described a way to use  artificial intelligence (AI) to facilitate the conversion of carbon dioxide (CO2) into methane.

By using this method to track the size, structure, and chemistry of catalytic particles under real reaction conditions, the scientists can identify which properties correspond to the best catalytic performance, and then use that information to guide the design of more efficient catalysts.

“Improving our ability to convert CO2 to methane would ‘kill two birds with one stone’ by making a sustainable non-fossil-fuel energy source that can be easily stored and transported while reducing carbon emissions,” said Anatoly Frenkel, a chemist with a joint appointment at the U.S. Department of Energy’s Brookhaven National Laboratory (BNL) and Stony Brook University. Frenkel is a professor of Materials Science in the College of Engineering and Applied Sciences.

Frenkel’s group has been developing a machine-learning approach to extract catalytic properties from x-ray signatures of catalysts collected as chemicals are transformed in reactions. The current analysis is described in a paper just published in the Journal of Chemical Physics, based on x-ray data collected at DOE’s Argonne National Laboratory.

The team of Argonne senior chemist Stefan Vajda, now at the J. Heyrovský Institute of Physical Chemistry in Prague, prepared size-selective clusters of copper atoms. Then they used mass spectrometry and x-rays at Argonne’s Advanced Photon Source (APS) to study how various size clusters performed in the reaction and how their oxidation state evolved during the reaction of carbon dioxide with hydrogen.

Copper has shown promise as a catalyst that can lower the temperature of the CO2-to-methane reaction. Size-selective copper clusters may also help drive the reaction efficiently to the desired outcome—selectively producing just methane and water vapor—without channeling reactants down a variety of pathways toward other products.

“There are, broadly speaking, two major challenges towards implementing this idea,” said Frenkel. “First is the lack of knowledge of the structure of the prepared clusters; the smaller they are, the more variations there may be in shapes and structures—even when the number of atoms in each cluster is the same.

“Second, even if we start the reaction with clusters of a certain size and shape, they may transform beyond recognition during the reaction to various forms of oxides,” Frenkel said.

The team also includes Stony Brook graduate students Nicholas Marcella and Yang Liu, as well as BNL chemist Ping Liu.

• Read more at the BNL site