CAREER: Breaking Scaling Relations under Realistic Operating Conditions for Efficient Catalytic Production of Fuels and Chemicals from CO2

About This Grant

A promising approach to decreasing the atmospheric burden of the greenhouse gas carbon dioxide (CO2) is to capture it from combustion sources and convert it to higher-value fuels and chemicals by using electrochemical cells powered by electricity generated from sustainable sources such as solar or wind energy. Catalysts are a key component of electrochemical reaction cells. The project combines theoretical, machine learning (ML) and experimental approaches to obtain fundamental knowledge critical to the design of highly efficient CO2 conversion catalysts. In addition, the project will provide early research opportunities for high school and undergraduate students, especially women and other underrepresented groups. A better understanding of chemical scaling relations and how to break them is urgently needed to achieve energy-efficient catalytic production of fuels and chemicals from CO2. However, understanding scaling relations and structure-property relationships under realistic operating conditions remains challenging. The project will utilize structurally precise catalysts as a testing platform and perform atomistic-scale simulations to reveal structures and catalytic properties under realistic reaction conditions. The first thrust will focus on catalytic scaling relations at the atomic level by developing and applying computational approaches (coupled with experimental validation) integrating quantum Monte Carlo, density functional theory, and molecular dynamics. Given the large chemical and structural space of these catalysts, the second thrust will couple fundamental insights with advanced machine learning techniques, including graph neural network models and interpretable machine learning methods, to efficiently design novel catalysts that break the identified scaling relations. The combined approach will be applied to structurally precise and atomically tunable catalysts (i.e., metal-nitrogen-doped carbon, atomically precise metal nanoclusters, and transition metal dichalcogenides) to explore the following three avenues for improving CO2 electrocatalytic reduction performance: I. Lowering the overpotential of the CO2 reduction reaction (CO2RR) through breaking the scaling relations between *CO and *CHO binding energies; II. Promoting C2+ products through breaking the scaling relations between *CO binding energy and C-C coupling barrier; and III. Suppressing the hydrogen evolution side reaction through breaking the scaling relations between *CO and *H binding energies. The project will deliver: (1) experimentally verifiable structure-activity relationships under operating conditions at the atomic level, (2) predictive computational methods and machine learning models for the study of electrocatalysis, and (3) highly active CO2RR catalysts with tunable selectivity. The project integrates research and education via workshops and training where discovery stimulates learning. Workshops will be hosted for grade 6-12 teachers, and relevant course modules will be developed for undergraduate students at Georgia Tech. 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.