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Rice, Penn State study shows machine learning can improve catalyst design

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. Their study is published in the journal Nature Catalysis.


Single-atom catalysts offer high reactivity and selectivity while maximizing utilization of the expensive active metal component. However, they are susceptible to sintering, where single metal atoms agglomerate into thermodynamically stable clusters. Tuning the binding strength between single metal atoms and oxide supports is essential to prevent sintering.

We apply density functional theory, together with a statistical learning approach based on least absolute shrinkage and selection operator regression, to identify property descriptors that predict interaction strengths between single metal atoms and oxide supports. Here, we show that interfacial binding is correlated with readily available physical properties of both the supported metal, such as oxophilicity measured by oxide formation energy, and the support, such as reducibility measured by oxygen vacancy formation energy. These properties can be used to empirically screen interaction strengths between metal–support pairs, thus aiding the design of single-atom catalysts that are robust against sintering.

—O’Connor et al.

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. 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.

—Rice’s Thomas Senftle, co-author

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. 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.

—Thomas Senftle

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. You can narrow your design space by using the computer to explore which materials are likely to do well under certain conditions.

—Thomas Senftle

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.

Binding energy between the metal and substrate is of particular interest because the stronger the bond, the less likely the metal atom is to dislodge, said Janik. “If we can control that binding energy, we can tailor the size distribution of these metal particles, and that, in turn, is going to impact the overall reaction that they can catalyze.

Along with the list of binding energies, the team had a catalog of about 330,000 additional properties for each of the metal-substrate combinations, including factors such as oxide formation energy, coordination number, alloy formation energy and ionization energy.

The machine learning algorithm looks for the combinations of those descriptors that correlate with the observed data on binding energies. It basically allows us to ask, ‘Of all of these descriptors, how can we find the ones that correlate with the observed behavior in which we’re interested?

—A.S.M. Jonayat

Jonayat said identifying such correlations can streamline catalyst design by making it possible to predict how materials will behave prior to laboratory testing that can be both expensive and time-consuming. Machine learning also can identify interesting effects that are worthy of additional study.

For example, Senftle said one correlation that kept appearing in the study was the importance of the direct interaction between the catalytic metals and the metal atoms in the support. He said this was unexpected because the metals typically each have a strong affinity to bind with oxygen as opposed to binding with each other.

Originally, the idea was that it was the oxygen that was important. We were interested in determining how well these two different metals shared the oxygen. But this direct interaction between the metals themselves kept popping up in our calculations, and it played a much larger role in dictating the overall behavior of the system than we had anticipated.

—Thomas Senftle

Senftle said he’d like to build on the complexity of the simulations in future research.

The research was supported by the National Science Foundation (NSF). Rice supercomputing resources were provided by the NSF-supported DAVinCI supercomputer administered by the Center for Research Computing and procured in partnership with Rice’s Ken Kennedy Institute for Information Technology.


  • Nolan J. O’Connor, A. S. M. Jonayat, Michael J. Janik & Thomas P. Senftle (2018) “Interaction trends between single metal atoms and oxide supports identified with density functional theory and statistical learning” Nature Catalysis doi: 10.1038/s41929-018-0094-5



Quantum computers are good for decoding, consider this one of those problems.


With quantum chemistry and machine learning, designing catalyst for new reactions will no longer be a tedious and trial and error process!

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