Accelerated discovery of CO2 electrocatalysts using active machine learning

Miao Zhong, Kevin Tran, Yimeng Min, Chuanhao Wang, Ziyun Wang, Cao-Thang Dinh, Phil De Luna, Zongqian Yu, Armin Sedighian Rasouli, Peter Brodersen, Song Sun, Oleksandr Voznyy, Chih-Shan Tan, Mikhail Askerka, Fanglin Che, Min Liu, Ali Seifitokaldani, Yuanjie Pang, Shen-Chuan Lo, Alexander Ip, Zachary Ulissi () and Edward H. Sargent ()
Additional contact information
Miao Zhong: University of Toronto
Kevin Tran: Carnegie Mellon University
Yimeng Min: University of Toronto
Chuanhao Wang: University of Toronto
Ziyun Wang: University of Toronto
Cao-Thang Dinh: University of Toronto
Phil De Luna: University of Toronto
Zongqian Yu: Carnegie Mellon University
Armin Sedighian Rasouli: University of Toronto
Peter Brodersen: University of Toronto
Song Sun: University of Science and Technology of China
Oleksandr Voznyy: University of Toronto
Chih-Shan Tan: University of Toronto
Mikhail Askerka: University of Toronto
Fanglin Che: University of Toronto
Min Liu: University of Toronto
Ali Seifitokaldani: University of Toronto
Yuanjie Pang: University of Toronto
Shen-Chuan Lo: Material and Chemical Research Laboratories
Alexander Ip: University of Toronto
Zachary Ulissi: Carnegie Mellon University
Edward H. Sargent: University of Toronto

Nature, 2020, vol. 581, issue 7807, 178-183

Abstract: Abstract 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 energy1,2. Particularly attractive is the electrochemical reduction of CO2 to chemical feedstocks, which uses both CO2 and renewable energy3–8. Copper has been the predominant electrocatalyst for this reaction when aiming for more valuable multi-carbon products9–16, 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. Here we describe 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 per cent (compared to about 66 per cent for pure Cu) is achieved at a current density of 400 milliamperes per square centimetre (at 1.5 volts versus a reversible hydrogen electrode) and a cathodic-side (half-cell) ethylene power conversion efficiency of 55 ± 2 per cent at 150 milliamperes per square centimetre. We perform computational studies that suggest that the Cu-Al alloys provide multiple sites and surface orientations with near-optimal CO binding for both efficient and selective CO2 reduction17. 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.

Date: 2020
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DOI: 10.1038/s41586-020-2242-8

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