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000890423 1001_ $$0P:(DE-HGF)0$$aMalek, Ali$$b0$$eCorresponding author
000890423 245__ $$aA Data-driven Framework for the Accelerated Discovery of CO2 Reduction Electrocatalysts
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000890423 520__ $$aSearching 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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000890423 7001_ $$0P:(DE-Juel1)129587$$aBaumann, Stefan$$b1
000890423 7001_ $$0P:(DE-Juel1)161591$$aGuillon, Olivier$$b2
000890423 7001_ $$0P:(DE-Juel1)178034$$aEikerling, Michael$$b3
000890423 7001_ $$0P:(DE-Juel1)181057$$aMalek, Kourosh$$b4$$eCorresponding author
000890423 773__ $$0PERI:(DE-600)2733788-1$$a10.3389/fenrg.2021.609070$$p609070$$tFrontiers in energy research$$v9$$x2296-598X$$y2021
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