A major challenge in renewable energy is the inefficient interconversion of electrical and chemical energy. New electrocatalysts are needed to solve this challenge. Two-dimensional (2D) materials are only one or a few atoms thick and as such, they serve as desirable, high surface area electrocatalysts for energy conversions. In this project, Dr. Yuanyue Liu is using computer simulations to provide a fundamental understanding of the capabilities of 2D materials as electrocatalysts. His simulations use artificial intelligence to accelerate the discovery process. Dr. Liu is training students in computational chemistry, catalysis and energy conversions by actively engaging them in multidisciplinary research and integrating his research findings into the classroom. Dr. Liu is working with the Faculty Technology Studio to create educational videos that illustrate electrocatalysis applications. These videos are being used in various outreach programs to increase the interest of K-12 students and underrepresented groups in pursuing science, technology, engineering and mathematics (STEM) careers.
With funding from the Chemical Catalysis Program of the Chemistry Division, Dr. Yuanyue Liu at the University of Texas at Austin is developing an improved understanding of the electrocatalytic mechanisms of 2D materials (an emerging class of electrocatalysts). By performing grand-canonical density functional theory (gc-DFT) calculations and coupling the calculations with machine learning, the team searches for new 2D electrocatalysts with better performance. Conventional DFT calculations often neglect the effects of varying charge and constant potential in electrochemical reactions, yet these parameters are found to be critical for 2D materials. The gc-DFT includes these effects and thus can elucidate the mechanisms of 2D electrocatalysts for reactions such as hydrogen evolution, oxygen reduction/evolution, and carbon dioxide reduction. Machine learning models are used to uncover the underlying factors that govern the catalytic activity and selectivity. The models also predict better catalysts from the large 2D materials library.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
