%0 Journal Article
%A Malek, Ali
%A Baumann, Stefan
%A Guillon, Olivier
%A Eikerling, Michael
%A Malek, Kourosh
%T A Data-driven Framework for the Accelerated Discovery of CO2 Reduction Electrocatalysts
%J Frontiers in energy research
%V 9
%@ 2296-598X
%C Lausanne
%I Frontiers Media
%M FZJ-2021-00943
%P 609070
%D 2021
%X 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.
%F PUB:(DE-HGF)16
%9 Journal Article
%U <Go to ISI:>//WOS:000644398900001
%R 10.3389/fenrg.2021.609070
%U https://juser.fz-juelich.de/record/890423