A Data-Driven Framework for the Accelerated Discovery of CO2 Reduction Electrocatalysts

Searching for next-generation electrocatalyst materials to use in electrochemical energy technologies is a time-consuming and prohibitively expensive process, even if it is enabled by high-throughput experimentation and large-scale first-principle calculations. In particular, the development of more active, selective and stable electrocatalysts for the CO2 reduction reaction remains tedious and challenging. Here, we demonstrate a material recommendation and screening framework, specifically adapted for certain classes of electrocatalyst materials for low or high-temperature CO2 reduction. The framework utilizes high level technical targets, advanced data extraction, and categorization paths and it recommends the most viable materials identified using data analytics and property-matching algorithms. Results indicate that different correlations govern catalyst performance under low and high-temperature conditions.