Narayanan receives Ralph E. Powe Junior Faculty Enhancement Award
Oct. 13, 2020
By Holly Hinson
Dr. Badri Narayanan, Assistant Professor of Mechanical Engineering at University of Louisville’s Speed School of Engineering, is one of the 35 recipients of the 2020 Oak Ridge Associated Universities (ORAU) Ralph E. Powe Junior Faculty Enhancement Award.
The competitive and prestigious Powe Award is given as public recognition for the quality and promise of research of junior faculty members and projects must fall within one of these five disciplines – engineering & applied science; life sciences; mathematics/computer sciences; physical sciences; or policy, management, and education.
Narayanan was one of the 167 applicants, nationally, to have been nominated by their university for competing for the Powe Award. As part of this award, ORAU provides $5,000 to support Narayanan’s research for one year. Additionally, the university will provide matching funds for a total of $10,000.
This award is in support of Narayanan’s research proposal titled “Coarse-grained graph neural-network models to design efficient routes for chemical upcycling of polymers.” This early stage research addresses an urgent societal need to identify new ways to recycle end-of-life plastics.
“Recent projections show that waste plastics will outweigh fishes in the oceans by 2050,” said Narayanan. “Clearly, we need to come up with innovative solutions to recycle plastics fast to protect our environment. Our proposal goes beyond just recycling plastics. It concerns chemical upcycling, which involves taking what is essentially trash – used plastics – and converting them into something more valuable, such as paint or starting materials for other organic compounds. Such a process is lucrative not only for environmental reasons, but also from an economic standpoint,” he said.
Narayanan explained that such conversion requires careful engineering of chemical reaction networks, and calls for new predictive computational tools. His group specializes in developing such tools using machine learning approaches and using atomistic modeling to advance the fundamental understanding of energy-relevant materials. “The idea is to develop material models using machine learning that can help us understand ways in which we can break down the big chain molecules into smaller chains – what catalysts we should use, and how to control the chemical reaction pathways.”
Narayanan will collaborate with staff scientists at the Leadership Computing Facility in Oak Ridge National Laboratory on this project. He anticipates that these new collaborations with Oak Ridge, the seed money from the grant, as well as the additional spotlight on his research from the award, will enable him to establish a successful research program in predictive modeling of materials.