Frontiers in Energy Research (Apr 2021)
A Data-Driven Framework for the Accelerated Discovery of CO2 Reduction Electrocatalysts
Abstract
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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