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Program speeds up complex chemistry research

The Exascale Catalytic Chemistry team, photographed in 2018 with the members at that time, is composed of researchers from Sandia, Argonne and Pacific Northwest national laboratories, as well as Brown and Northeastern universities. Credit: Dino Vournas A successful partnership to help make aspects of chemistry research faster and more productive was recently renewed for another…

Program helps speed up research of complex chemistry problems
The Exascale Catalytic Chemistry team, photographed in 2018 with the members at that time, is composed of researchers from Sandia, Argonne and Pacific Northwest national laboratories, as well as Brown and Northeastern universities. Credit: Dino Vournas

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

The Exascale Catalytic Chemistry project with Sandia, Argonne and Pacific Northwest national laboratories, as well as Brown and Northeastern universities, started in 2017 and brings together physical chemists and applied mathematicians to design to take advantage of the most powerful computers in the world to speed up understanding of heterogeneous catalysis, a complex chemistry problem.

Gas-phase molecules transformed on metal surfaces

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

” This project is a contribution to catalysis research. It automates the creation of complex models necessary to describe the complicated chemistry between gases. “Even for seemingly simple systems, like the hydrogenation of CO and CO2, there can be many dozens of reactions that take place on a simple facet of a metal. If we take into account larger molecules and more complicated surfaces, this can lead to many more reactions. “

Engineers and chemists are actively studying these interactions, including the conversion of cheaper, simpler molecules to more valuable, more expensive ones. Judit’s Sandia team and others can now create models and model these reactions more efficiently and systematically using the newly developed tools.

” People have traditionally assembled these reaction mechanisms by manually enumerating the relevant reactions as best they can and then calculating the properties for each one individually. Judit stated that it is a slow and error-prone process.

” Our partners at Northeastern and Brown created a computer program that can count the reactions and give you an estimate of their properties. “At Sandia, we create codes to automatically and systematically study these reactions with quantum chemistry. To interpret the models, we also created simulation and analysis tools. Pacific Northwest National Laboratory contributes with its expertise in quantum chemistry, while Brown, Argonne, and Sandia work together to improve thermochemistry. “

Improving chemistry one bit at a time

The project’s goal is to uncover interesting science about specific systems and give researchers tools that allow them to more accurately predict their systems. This will enable them to focus their experimental efforts on catalytic strategies that are most effective. These systems can be used to predict the interactions that will result in a desired chemical reaction.

Judit stated that determining which interactions are the most important to model is like deciding which branch of a tree you should trim to get the shape you desire.

“There are always chemical pathways to get you what you want. But there are also pathways that lead you to a product that you don’t like.” she stated. If you think of the tree, one branch can lead to the desired outcome. The other branches will lead to something else. With enough computational power and an automated tool, it is possible to examine more scenarios than was previously theoretically or experimentally possible. This will help you understand how a catalytic reaction produces a product. “

A big reason why chemistry researchers need too

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