A Data-Driven Framework for the Accelerated Discovery of CO2 Reduction Electrocatalysts

Ali Malek, Qianpu Wang, Stefan Baumann, Olivier Guillon, Michael Eikerling, Kourosh Malek
<span title="2021-04-13">2021</span> <i title="Frontiers Media SA"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/kr4gvkhrzfe5he2vhzlh4x753e" style="color: black;">Frontiers in Energy Research</a> </i> &nbsp;
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
more &raquo; ... ain 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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