A catalyst for change: New research aims to design atomically efficient and selective catalysts
Virginia Tech researchers collaborate to create more environmentally friendly and efficient catalysts with the help of artificial intelligence and machine learning.
Most of us understand that electrical engineering and mechanical engineering play a key role in running our washing machines or our computers. But did you know that more than 80 percent of the products we use every day, such as fertilizers, cosmetics, fragrances, rubber and more, require some sort of chemical catalyst while being manufactured?
Catalysts are like turbochargers for chemical reactions. Until now, the process of designing a catalyst for chemical reactions has been mostly trial and error. Researchers from Virginia Tech are working to change that.
A team led by chemical engineering Professor Ayman Karim has been awarded $1.8 million from the National Science Foundation to observe how single atom catalysts change and evolve during a reaction. The goal is to capture how artificial intelligence (AI) can help model these small chemical structures and structures and build high-fidelity models that can be used on a larger, more complex scale in the future.
“Designing highly selective catalysts is important because it will allow us to more efficiently use and eventually transition away from fossil fuels,” said Karim. “If we can figure out how to develop a methodology to design a catalyst for simple reactions, we will be able to use that as a foundation for other reactions and even eventually search for optimal catalysts in a database.”
The research project brings together three principal partners:
- Virginia Tech’s College of Engineering will lead the synthesis of isolated metal atoms and advanced characterization to determine the catalyst structure and electronic propertiestheir characterization using microcalorimetry and in-situ/operando spectroscopy and microscopy.
- University of Delaware’s Professor Dionisios Vlachos will develop AI and machine learning to model the catalyst combinations needed for specific chemical reactions.
- University of Pennsylvania Professors John Vohs and Raymond Gorte will create thin films of unconventional metal oxides oxides to understand how they interact and modulate the properties of isolated metal atoms, such as rhodium and platinum.
Karim and the partnering research teams are eager to demonstrate how the use of AI in this can have important environmental benefits for future projects involving chemical reactions.
“By utilizing machine learning in the field of chemical engineering, and applying it to catalysis in particular, we are aiming to dramatically reduce carbon dioxide emissions and lower energy consumption,” said Karim. “Catalysis is used in several industries including the automotive and chemical industry. By reducing the impact and increasing efficiency by utilizing every metal atom in the catalyst, those benefits boil down to the consumer as well.”
The four-year project is part of the National Science Foundation’s recently announced Designing Materials to Revolutionize and Engineer our Future program. The $72.5 million initiative supports projects that “drive the design, discovery, and development of advanced materials needed to address major societal challenges.”