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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Bibliographic Details
Published in:Frontiers in energy research 2021-04, Vol.9
Main Authors: Malek, Ali, Wang, Qianpu, Baumann, Stefan, Guillon, Olivier, Eikerling, Michael, Malek, Kourosh
Format: Article
Language:English
Subjects:
artificial intelligence
CO2 reduction
data analytics
high and low temperature
machine learning
materials discovery
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Summary: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 CO 2 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 CO 2 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.
ISSN:2296-598X
2296-598X
DOI:10.3389/fenrg.2021.609070