Chemists Develop New Machine Learning Framework to Improve Catalysts |

BNL Wenjie Liao
Bnl wenjie liao
Stony Brook graduate student Wenjie Liao and Brookhaven Lab chemist Ping Liu developed a machine learning framework to identify chemical reaction steps that could be targeted to improve reaction productivity.

Chemists at Brookhaven National Laboratory have developed a new machine learning (ML) framework that can focus on which steps in a multi-step chemical conversion need to be changed to improve productivity. The approach could help guide the design of catalysts – chemical “negotiators” that speed up reactions.

“Our goal was to identify which elementary step of the reaction network or which subset of steps controls the catalytic activity,” said Wenjie Liao, the first author of a paper describing the method just published. in the journal. Catalysis Science and Technology. Liao is a Stony Brook University graduate student who has worked with scientists in the Reactivity and structure of catalysis (CRS) from the Chemistry Division of Brookhaven Lab.

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The team developed the method to analyze the conversion of carbon monoxide to methanol using a copper-based catalyst. The reaction consists of seven fairly simple elementary steps.

Ping Liu, the CRS chemist who led the work, said, “We used this reaction as an example of our ML framework method, but you can put any reaction in this framework in general.”

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For more information on research, see Brookhaven Lab Press Release.

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