Study shows machine learning can improve catalytic design | Phys.org

Chemical engineers at Rice University and Pennsylvania State University have shown that combining machine learning and quantum chemistry can save time and expense in designing new catalysts, as Phys.org reports.
 

A quantum chemical simulation (lower panel) depicts the charge transfer (blue/green) between metal atoms and an underlying support (orange). This is but one description of a catalyst's physical behavior, and researchers created a massive database by calculating 330,000 such descriptions for each of many catalysts. Machine learning was used (upper panel) to search the database for hidden patterns that designers can use to make cheaper, more efficient catalysts.
Photo: Tom Senftle/Rice University

"Large amounts of data are generated in computational catalysis, and the field is starting to realize that data science tools can be extremely valuable for sifting through high-volume data to look for fundamental correlations that we might otherwise miss," said Rice's Thomas Senftle, co-author of a new study published online this week in Nature Catalysis. "That's what this paper was really about. We combined well-established tools for data generation and analysis in a way that allowed us to look for correlations we wouldn't otherwise have noticed."

A catalyst is a substance that accelerates chemical reactions without being consumed by them. The catalytic converters in automobiles, for example, contain metals like platinum and palladium that aid in reactions that break down air pollutants. Catalysts are a mainstay of the chemical and pharmaceutical industries, and the global market for catalysts is estimated at $20 billion per year.

The metals used in catalytic converters are typically part of a wire mesh. As hot exhaust passes through the mesh, the metal atoms on the surface catalyze reactions that break apart some noxious molecules into harmless byproducts.

"That's a gas phase reaction," Senftle said of the catalytic converter example. "There's a certain concentration of gas-phase species that come out of the engine. We want a catalyst that converts pollutants into harmless products, but different cars have different engines that put out different compositions of those products, so a catalyst that works well in one situation may not work as well in another."

The practice of flowing reactants past a catalyst is also common in industry. In many cases, a catalytic metal is attached to a solid surface and reactants are flowed over the surface, either as a liquid or a gas. For industrial processes that make tons of products per years, improving the efficiency of the metal catalyst by even a few percent can translate into millions of dollars for companies.

"If you have a clear picture of the properties of the metal catalyst and the substrate material the metal attaches to, that allows you to basically narrow down your search at the beginning," Senftle said. "You can narrow your design space by using the computer to explore which materials are likely to do well under certain conditions."

Senftle, assistant professor in chemical and biomolecular engineering at Rice, began the newly published research while still a graduate student at Penn State in 2015, along with lead authors Nolan O'Connor and A.S.M. Jonayat and co-author Michael Janik. They started by using density functional theory to calculate the binding strengths of single atoms of many different kinds of metals with a range of metal oxide substrates.  
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Additional resources   
Nolan J. O'Connor et al, Interaction trends between single metal atoms and oxide supports identified with density functional theory and statistical learning, Nature Catalysis (2018). DOI: 10.1038/s41929-018-0094-5   

Source: Phys.org