The Royal Institution for the Advancement of Learning/McGill University

Published purpose

The Project seeks to advance CO2-recycling via electrocatalysis using AI-driven mapping and engineering of key reaction centres at the atomic scale. Copper is the most effective CO2-to-fuel electrocatalyst. However, no systematic approach has yet been developed to engineer its governing reaction mechanisms. To address this gap, the Project team will employ AI to accelerate the design of chemically active sites and interfacial solvation structures on copper during CO2 conversion. The ultimate aim is to utilize Artificial Intelligence (AI) driven data acquisition and design inferences to accelerate the discovery of engineering solutions to improve both the efficiency and long term stability of copper electrode based technology. This will be accomplished by coupling AI, operando experiments, and supporting modeling at the atomic scale.