Big Data Meets Small Catalysts To Tease Out Reaction Mechanisms
 | February 16, 2015 Issue - Vol. 93 Issue 7 | Chemical & Engineering News
Volume 93 Issue 7 | p. 8 | News of The Week
Issue Date: February 16, 2015 | Web Date: February 12, 2015

Big Data Meets Small Catalysts To Tease Out Reaction Mechanisms


Organic Chemistry: Chemists bring data-intensive approach to bear on chiral anion Catalysts
Department: Science & Technology
News Channels: Organic SCENE
Keywords: big data, physical chemistry, computational chemistry, chiral anion, mechanism
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Graduate students Milo (left) and Neel videoconference to decipher a reaction mechanism.
Credit: Cristiane Storck Schwalm
University of Utah graduate student Anat Milo videoconferences with UC-Berkeley graduate student Andrew Neel for a collaboration to determine the mechanism of chiral anion catalysis.
 
Graduate students Milo (left) and Neel videoconference to decipher a reaction mechanism.
Credit: Cristiane Storck Schwalm

By combining modern data analysis techniques with classical physical organic and computational chemistry, chemists have developed a way to pin down the mechanism by which a chiral anion catalyst generates certain enantiomers. The method could help chemists rationally design more effective catalysts.

By figuring out a reaction’s mechanism—the precise way the reactants come together to form products—chemists can learn how to tweak that transformation to improve upon it, by boosting the yield, for example. But mechanisms can be complicated, particularly those of enantioselective catalysts in which myriad attractive and repulsive nonbonding forces are at work.

To get a better handle on what was happening in their reaction flasks, University of California, Berkeley, chemists F. Dean Toste and Andrew J. Neel teamed up with University of Utah chemists Matthew S. Sigman and Anat Milo. Together, they took a data-intensive look at an intramolecular dehydrogenative C–N coupling reaction that’s catalyzed by chiral phosphoric acid derivatives (Science 2015, DOI: 10.1126/science.1261043).

Neel performed dozens of permutations of the reaction, tweaking both catalyst and substrate, and then shared his data with Milo, who applied modern data analysis techniques to them. “What they came up with,” Sigman says, “was a model to describe what look like very interesting interactions between substituents on both the catalyst and the substrate.” On the basis of that information, the chemists designed new catalysts, ultimately predicting how they would behave.

“You’ve got all sorts of stuff going on in this reaction, which makes it difficult to figure out with classic kinetics,” Toste says. “But here’s an approach where you take every bit of data you’ve got and you build a model based on physical parameters and combine that with classical physical organic chemistry. Then you’ve got something that’s really powerful that I think anybody could use.”

Steven E. Wheeler, a computational chemistry expert at Texas A&M University, says, “Toste and Sigman have shown that a data-driven approach to mechanistic analyses can complement traditional tools of physical organic chemistry, providing a key step toward a future in which big data is used to design small catalysts.”

 
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