The program helps speed up the study of complex chemistry problems

Filmed with the members of the time in 2018, the Exascale Catalytic Chemistry team consists of Sandia, Argonne, the National Institute of the Northwest Pacific, and researchers from Brown University and Northeastern University. Credit: Dino Vournas

Successful partnerships to help make aspects of chemical research faster and more productive have been updated for another four years.

The Exascale Catalytic Chemistry Project with Sandia, Argonne, Pacific Northwest National Laboratory, and Brown University and Northeastern University was launched in 2017 and was designed by bringing together physical chemists and applied mathematicians. Calculation tool Use the world’s most powerful computers to speed up your understanding of heterogeneous catalysis, a complex chemistry problem.

Gas phase molecules transformed on the metal surface

Judit Zádor, project director of Exascale Catalytic Chemistry, has formed a team of experts to develop a model of heterogeneous catalysis (reaction of gas phase molecules). Metal surface— Faster and more reliable.

“What this project brings to catalyst research is to try to automate the creation of the complex models needed to describe the complex chemistry between the gas and the catalyst surface,” says Judit. “Even in a seemingly simple system like CO or hydrogenation of CO2, Reactions that occur on simple surfaces of metal can range to dozens. Considering larger molecules and more complex surfaces, this can grow to hundreds or more. “

Chemists and engineers are actively studying these interactions, such as converting simpler and cheaper molecules into more useful and expensive molecules. With the new tools developed, Judit’s team since Sandia can create models and simulate these reactions more easily and systematically.

“People traditionally assemble these reaction mechanisms by manually enumerating the relevant reactions as manually as possible and calculating the characteristics of each reaction individually. This is a slow process and error-prone. It’s possible, “says Judit.

“Brown and Northeastern partners have created computer code that can enumerate the reactions and estimate their characteristics in a systematic way,” continued Judit. “Sandia has written code to systematically and automatically study these reactions using quantum chemistry, as well as simulation and analysis tools to interpret the entire model. Northwest Pacific. The National Institute is contributing with its underlying quantum chemistry expertise, while Brown, Argonne and Sandia are working together to develop new ways to improve thermochemistry. “

Improve chemistry one bit at a time

In addition to revealing interesting science for a particular system, the project’s key goal is to more accurately predict the system of interest to other researchers and ultimately experiment with the most productive catalytic strategies. It’s about providing tools that allow you to focus your efforts. These systematic calculations allow us to more accurately predict which interactions will lead to the desired chemical reaction.

Judit said finding the most important interactions to model is similar to knowing which branch of a tree to prune into the desired shape.

“On the surface of the catalyst, there are always chemical pathways that go where you want, but there are pathways that end up in products that you don’t want,” she said. “Imagine a tree, you can follow one branch to the right, which leads to correct results, but to the left, it leads to undesired results. With automated tools and sufficient computing power. It helps us to consider far more scenarios than traditional theoretically or experimentally possible scenarios and to understand why a particular product is produced by a catalytic reaction. “

The main reason chemists need the tools provided by high performance computing is that there are so many possible reactions to measure or calculate.

“Recently, we can afford to make accurate calculations for more of the top reactions, as well as some of the most important ones, and we’ve improved our kinetics estimates,” says Judit. “The strategy of this project is to repeatedly improve the model. Suggest mechanisms, select the most important but least known parts, improve them, and then plug into the original mechanism. If this gives you a better mechanism and it’s still not enough, do another round. This cyclical improvement is an important concept in this project. If you go around enough times, you’ll get the accuracy you want. Must be achieved. “

Next stage

How Judit and her team worked as the Exascale Catalytic Chemistry project, funded by DOE’s Department of Science, Basic Energy Sciences, Chemistry, Earth Sciences and Biological Sciences, was updated for another four years. Chemistry The amount of a particular molecule on the surface of the catalyst depends on the presence of other molecules on the surface.

“These so-called co-adsorbents are important because they change the outcome of the reaction. However, there are too many ways in which these molecules interact on the surface, which complicates the calculations of these systems.” “Judit said. “You can’t do that manually, and it seems that you can’t do it with the power of your computer alone. You’ll have to use machine learning to take advantage of the computational framework. This is an exciting challenge. . ”

Moving the holes will increase the productivity of the catalyst

Quote: The program is a study of complex chemistry problems (January 15, 2022) obtained from https: // on January 15, 2022. Helps to speed up

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The program helps speed up the study of complex chemistry problems

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