Researchers Up-Cycle Waste Carbon with Record Efficiency Using AI

Sunday, May 24, 2020 - 16:28

Researchers at University of Toronto Engineering and Carnegie Mellon University could accelerate progress in transforming waste carbon into a commercially valuable product with record efficiency by using artificial intelligence (AI).

According to the SciTech Daily report, they leveraged AI to speed up the search for the key material in a new catalyst that converts carbon dioxide (CO2) into ethylene — a chemical precursor to a wide range of products, from plastics to dish detergent.

The resulting electrocatalyst is the most efficient in its class. If run using wind or solar power, the system also provides an efficient way to store electricity from these renewable but intermittent sources.

“Using clean electricity to convert CO2 into ethylene, which has a $60 billion global market, can improve the economics of both carbon capture and clean energy storage,” says Professor Ted Sargent, one of the senior authors on a new paper published on May 13, 2020, in Nature.

Sargent and his team have already developed a number of world-leading catalysts to reduce the energy cost of the reaction that converts CO2 into ethylene and other carbon-based molecules. But even better ones may be out there, and with millions of potential material combinations to choose from, testing them all would be unacceptably time-consuming.

The team showed that machine learning can accelerate the search. Using computer models and theoretical data, algorithms can toss out worst options and point the way toward more promising candidates.

Reference: “Accelerated discovery of CO2 electrocatalysts using active machine learning” by 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, 13 May 2020, Nature.


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