Accelerated discovery of CO electrocatalysts using active machine learning.

The rapid increase in global energy demand and the need to replace carbon dioxide (CO)-emitting fossil fuels with renewable sources have driven interest in chemical storage of intermittent solar and wind energy. Particularly attractive is the electrochemical reduction of CO to chemical feedstocks, which uses both CO and renewable energy. Copper has been the predominant electrocatalyst for this reaction when aiming for more valuable multi-carbon products, and process improvements have been particularly notable when targeting ethylene.