Congratulations to Professor Zhong Miao on Publishing a Research Article in Nature
Accelerated discovery of CO2 electrocatalysts using active machine learning
The rapid increase in global energy demand and the need to replace carbon dioxide (CO₂)-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 CO₂ to chemical feedstocks, which uses both CO₂ 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. However, the energy efficiency and productivity (current density) achieved so far still fall below the values required to produce ethylene at cost-competitive prices.
This article describes Cu-Al electrocatalysts, identified using density functional theory calculations in combination with active machine learning, that efficiently reduce CO₂ to ethylene with the highest Faradaic efficiency reported so far. This Faradaic efficiency of over 80% (compared to about 66% for pure Cu) is achieved at a current density of 400 milliamperes per square centimeter (at 1.5 volts versus a reversible hydrogen electrode) and a cathodic-side (half-cell) ethylene power conversion efficiency of 55 ± 2% at 150 milliamperes per square centimeter.
Computational studies suggest that the Cu-Al alloys provide multiple sites and surface orientations with near-optimal CO binding for both efficient and selective CO₂ reduction. Furthermore, in situ X-ray absorption measurements reveal that Cu and Al enable a favourable Cu coordination environment that enhances C–C dimerization. These findings illustrate the value of computation and machine learning in guiding the experimental exploration of multi-metallic systems that go beyond the limitations of conventional single-metal electrocatalysts.
The related research findings were published in Nature under the title “Accelerated discovery of CO2 electrocatalysts using active machine learning”.
Article link: https://doi.org/10.1038/s41586-020-2242-8