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100 1 _ |a Malek, Ali
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245 _ _ |a A Data-driven Framework for the Accelerated Discovery of CO2 Reduction Electrocatalysts
260 _ _ |a Lausanne
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520 _ _ |a 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.
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700 1 _ |a Baumann, Stefan
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700 1 _ |a Guillon, Olivier
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700 1 _ |a Eikerling, Michael
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700 1 _ |a Malek, Kourosh
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773 _ _ |a 10.3389/fenrg.2021.609070
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