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In the News: UMBC Advance ChemCatBio Research

Novel Symbolic Regression To Speedup Surface Chem Simulation

Excerpt from:

Two Minority Serving Universities Advance ChemCatBio Research Priorities With New Funding

~~ Published in: January, 2024
ChemCatBio, ChemCatBio

Catalyst deactivation and slow computational research methods are recognized barriers for rapidly moving catalyst-driven bioenergy technologies from discovery to scale-up. But researchers are closer to mitigating both challenges thanks to two university-led projects in partnership with the Chemical Catalysis for Bioenergy Consortium (ChemCatBio).

The University of New Mexico and University of Maryland, Baltimore County were awarded funding from the U.S. Department of Energy Bioenergy Technologies Office and the Minority Serving Institution STEM Research & Development Consortium as part of a funding call for ChemCatBio. The funding partnership aims to reduce barriers of entry for minority serving institutions and increase bioenergy research collaboration.

According to ChemCatBio Director Josh Schaidle, the projects are part of a consortium strategy to synchronize catalyst innovation and diversity, equity, and inclusion.

"We are excited to partner with both universities and tap the unique expertise they bring to addressing catalyst deactivation and speeding catalyst discovery," he said. "These diverse institutions, people, and perspectives are essential to realizing the vision of ChemCatBio, which is the rapid decarbonization of our economy."

University of Maryland, Baltimore County - Applying a Novel Symbolic Regression To Speed Up Surface Chemistry Simulations

A collage of the headshots of three women and two men
From left: Tyler Josephson, Kianoush Ramezani Shabolaghi, Samiha Sharlin, Charishma Puli, and Fariha Agbere. Photos courtesy of Tyler, Kianoush, Samiha, Charishma, and Fariha, respectively.


Tyler Josephson, assistant professor
Kianoush Ramezani Shabolaghi, chemical engineering Ph.D. candidate
Samiha Sharlin, Ph.D. candidate
Charishma Puli, data science M.S. student
Fariha Agbere, chemical engineering B.S. student

ChemCatBio researchers are developing methods for upgrading biomass into a feedstock of mixed olefins, which can be upgraded into energy-dense sustainable aviation fuel using zeolite catalysts. However, questions remain on how the molecular shape of the catalyst - especially the porosity of the catalyst - affects the efficiency of those reactions.

In the past, quantum chemistry has been used to study such chemical reactions, but those methods are slow and expensive when scaled up to large systems. To speed up the rate of discovery, a team from the Department of Chemical, Biochemical, and Environmental Engineering at University of Maryland, Baltimore County are using machine learning to learn how the interactions work at the quantum chemistry scale.

"With our new methods, we aim to study larger systems and more realistic conditions," explained Tyler Josephson, the principal investigator, who was recently awarded a NSF career award. "If we can predict in the computer that this zeolite architecture works better than that zeolite architecture, that's useful information for experimentalists."

Josephson said that the project complements broader research work in the ATOMS Lab to bring machine learning and automated reasoning into chemical engineering.

Posted: January 11, 2024, 2:30 PM