期刊
APPLIED CATALYSIS B-ENVIRONMENTAL
卷 339, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.apcatb.2023.123128
关键词
Fuel cells; Electrocatalyst; Ternary alloy; Machine learning; Catalyst design protocol
Researchers have developed a machine learning-driven catalyst screening protocol to identify ternary electrocatalysts for fuel cells that are potentially synthesizable, lower-cost, and higher-performance than conventional Pt materials. Experimental results showed that one of the final candidates, Cu3Au@Pt, outperformed Pt materials in oxygen reduction reactions. This approach demonstrates the usefulness of machine learning in exploring multicomponent systems and catalysis problems.
Despite their potential promise, multicomponent materials have not been actively considered as catalyst mate-rials to date, mainly due to the massive compositional space. Here, targeting ternary electrocatalysts for fuel cells, we present a machine learning (ML)-driven catalyst screening protocol with the criteria of structural sta-bility, catalytic performance, and cost-effectiveness. This process filters out only 10 and 37 candidates out of over three thousand test materials in the alloy core@shell (X3Y@Z) for each cathode and anode of fuel cells. These candidates are potentially synthesizable, lower-cost and higher-performance than conventional Pt. A thin film of Cu3Au@Pt, one of the final candidates for oxygen reduction reactions, was experimentally fabricated, which indeed outperformed a Pt film as confirmed by the approximately 2-fold increase in kinetic current density with the 2.7-fold reduction in the Pt usage. This demonstration supports that our ML-driven design strategy would be useful for exploring general multicomponent systems and catalysis problems.
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