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Monday, January 24, 2022

The program helps accelerate the research of complex chemistry problems

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The Exascale Catalytic Chemistry team, photographed in 2018 with members at the time, includes researchers from the Sandia, Argonne and Pacific Northwest national laboratories as well as Brown and Northeastern universities. credit: Dino Vornas

A successful partnership was recently renewed for another four years to help make aspects of chemistry research faster and more productive.


The Exascale Catalytic Chemistry Project, with Sandia, Argonne and Pacific Northwest National Laboratories, as well as Brown and Northeastern Universities, began in 2017 and provides physical chemists and applied mathematicians the ability to design computational tools to take advantage of the world’s most powerful computers. brings together. Accelerating the understanding of heterogeneous catalysis, a complex chemistry problem.

Changed gas-phase molecules on metal surfaces

Judit Zador, project director for Exascale Catalytic Chemistry, assembled the team of experts to develop models for catalysis – reactions of gas-phase molecules that occur on metal surfaces – faster and more reliably.

“What this project brings to catalyst research is that it seeks to automate the creation of the complex models that are needed to describe the complex chemistry between gases and the catalyst surface,” Judit said. “Even for seemingly simple systems, such as the hydrogenation of CO and CO2, many dozens of reactions can occur on a simple facet of the metal. If we consider larger molecules and more complex surfaces, this can increase to hundreds or more.”

Chemists and engineers actively study these interactions in problems, including transforming simple, cheap molecules into more useful, expensive molecules. With the new tools developed, Judit’s team at Sandia and beyond can model and simulate these reactions more easily and systematically.

Judit said, “People traditionally assemble these reaction mechanisms by trying to calculate the best-related reactions manually, and then calculate the properties for each reaction individually. This is a slow process.” The process is and can be error prone.”

“Our partners at Brown and Northeastern created a computer code that can calculate responses and infer their properties for you in a systematic way,” Judit continued. “At Sandia we then systematically code, yet automatically, study these reactions using quantum chemistry. We have also built simulation and analysis tools to fully interpret the model. Pacific Northwest National Laboratory contributes from its expertise in the underlying quantum chemistry method, while Brown, Argonne and Sandia jointly develop new methods to improve thermochemistry.”

improve chemistry one by one

In addition to uncovering interesting science about particular systems, an important goal of the project is to give other researchers the tools to more accurately predict their systems of interest and ultimately focus experimental efforts on the most productive catalytic strategies. can focus. These systematic calculations can more accurately predict which interactions will lead to the desired chemical reaction.

Judit said knowing which interactions are most important to the model is akin to knowing which branch of the tree to cut to take the shape you want.

“There are always chemical pathways on a catalyst surface that end where you want, but there are pathways that end with a product that you don’t want,” she said. “If you imagine a tree, you can follow a branch to the right, and it leads to the right result, but follow to the left, and it leads to an undesirable outcome. If your Having an automated tool and sufficient computational power, you can investigate many more scenarios than is traditionally possible theoretically or experimentally and help you understand whether a catalytic reaction produces a product. “

A big reason chemistry researchers need the tools provided by high-performance computing is that there are so many possible reactions to measure or calculate.

“These days we can afford to make accurate calculations not only for the top few most important responses but for many more, and we get better response rate estimates,” Judit said. “The strategy for this project is to improve the model iteratively. You propose a mechanism, you select the most important but least known parts, you improvise them and then you plug it into the original system. Now you have a better mechanism, and if it’s still not good enough, you make another round. This circular correction is a key concept of this project. If you spin long enough you should get your desired accuracy needed.”

Next step

Now that the Exascale Catalytic Chemistry Project—funded by DOE’s Office of Science, Division of Basic Energy Sciences, Chemistry, Geology and Biosciences—was renewed for another four years, Judit and her team want to study whether How is the chemistry of a given molecule on one. The catalyst surface is altered by the presence of other molecules on one surface.

“These so-called co-adsorbates change the outcome of the reactions, so they are important. However, setting up the calculations for these systems leads to extreme complexity, as there are many ways in which these molecules can interact at the surface.” Judit said. said. “You can’t do it by hand, and it seems you can’t even do it with just the power of a computer. We have to use machine learning to leverage our computational framework. It’s an exciting challenge.” ”


Migrating holes help catalysts be productive


Provided by Sandia National Laboratories

Citation: Program Helps Accelerate Research of Complex Chemistry Problems (2022, January 15) Retrieved 15 January 2022 from https://phys.org/news/2022-01-complex-chemistry-problems.html .

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