This title appears in the Scientific Report :
2021
Please use the identifier:
http://dx.doi.org/10.3389/fenrg.2021.609070 in citations.
Please use the identifier: http://hdl.handle.net/2128/27826 in citations.
A Data-driven Framework for the Accelerated Discovery of CO2 Reduction Electrocatalysts
A Data-driven Framework for the Accelerated Discovery of CO2 Reduction Electrocatalysts
Searching for next-generation electrocatalyst materials for electrochemical energy technologies is a time-consuming and expensive process, even if it is enabled by high-throughput experimentation and extensive first-principle calculations. In particular, the development of more active, selective and...
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Personal Name(s): | Malek, Ali (Corresponding author) |
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Baumann, Stefan / Guillon, Olivier / Eikerling, Michael / Malek, Kourosh (Corresponding author) | |
Contributing Institute: |
JARA-ENERGY; JARA-ENERGY Werkstoffsynthese und Herstellungsverfahren; IEK-1 IEK-13; IEK-13 |
Published in: | Frontiers in energy research, 9 (2021) S. 609070 |
Imprint: |
Lausanne
Frontiers Media
2021
|
DOI: |
10.3389/fenrg.2021.609070 |
Document Type: |
Journal Article |
Research Program: |
Chemische Energieträger |
Link: |
OpenAccess OpenAccess |
Publikationsportal JuSER |
Please use the identifier: http://hdl.handle.net/2128/27826 in citations.
Searching for next-generation electrocatalyst materials for electrochemical energy technologies is a time-consuming and expensive process, even if it is enabled by high-throughput experimentation and extensive first-principle calculations. In particular, the development of more active, selective and stable electrocatalysts for the CO2 reduction reaction remains tedious and challenging. Here, we introduce a material recommendation and screening framework, and demonstrate its capabilities for certain classes of electrocatalyst materials for low or high-temperature CO2 reduction. The framework utilizes high-level technical targets, advanced data extraction, and categorization paths, and it recommends the most viable materials identified using data analytics and property-matching algorithms. Results reveal relevant correlations that govern catalyst performance under low and high-temperature conditions. |