The Governors of the University of Calgary

Published purpose

Electrochemical CO2 reduction is a one-of-a-kind approach to mitigate atmospheric CO2 concentration and generate carbon neutral fuels or industrial chemicals. At the cost of high overpotentials and low selectivity, copper-based catalysts are currently being extensively examined for electrochemical conversion of CO2 to methane and ethylene. Discovering novel catalytic materials that can overcome the challenges of commonly used copper-based catalysts has been a long-standing goal. The Project combines advanced experimental techniques, density functional theory (DFT) and machine learning (ML) to develop and identify active, stable, and selective catalyst materials for CO2 reduction reaction (CO2RR). The Project will also result in an improved Orbital Crystal Graph Convolutional Neural Network (OGCNN) model. Improved features of the model include orbital interaction and electronic structure attributes such as Fukui Function. New catalyst materials designed by the improved OGCNN model will be experimentally tested to confirm improved performance in CO2RR.