Accelerated discovery of CO2 electrocatalysts using active machine learning

By M. Zhong, Kevin Tran, Yimeng Min, Chuanhao Wang, Ziyun Wang, C. Dinh, Phil de Luna, Zongqian Yu, A. Rasouli, P. Brodersen and 12 more. Nature, 2020

1,213 citations recorded by the source corpus.

Subfields: Materials Chemistry, Renewable Energy, Sustainability and the Environment

View Paper: https://www.semanticscholar.org/paper/a3a4dc32aa5ad355fb1e512a67baaed8102ed551

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Materials Chemistry

1. P. Strasser
2. Alán Aspuru-Guzik
3. Karen Chan
4. Edward H. Sargent
5. S. Boettcher
6. B. Roldán Cuenya
7. Matthias Kästner
8. Z. Seh

In Renewable Energy, Sustainability and the Environment

1. P. Strasser
2. Karen Chan
3. A. Varela
4. B. Roldán Cuenya
5. I. Stephens
6. J. Rossmeisl
7. M. Koper
8. A. Bagger

Citations in the corpus

• Technologies and perspectives for achieving carbon neutrality (2021, background): “Raising water tables and avoiding draining peatlands should be executed to conserve the vital C stored in peatlands.(192)”
• Machine learning for a sustainable energy future (2022, background): “Experimental validation 76 with Cu-Al catalysts yielded an unprecedented Faradaic efficiency of 80 % towards ethylene at a high current density of 400 mA cm -2 (Fig.”
• 2022 roadmap on low temperature electrochemical CO2 reduction (2022, methodology): “Reprinted by permission from Springer Nature Customer Service Centre GmbH: Springer Nature, Nature Nanotechnology [119], Copyright (2019). capillary DEMS setup employed complex mixed reactant gas mixtures, partial pressures, as well as isotope-labeled 13CO/12CO2 co-feeds.”