Park, Youngtae Hwang, Chang-Kyu Bang, Kihoon Hong, Doosun Nam, Hyobin Kwon, Soonho Yeo, Byung Chul Go, Dohyun An, Jihwan Ju, Byeong-Kwon Kim, Sang Hoon Byun, Ji Young Lee, Seung Yong Kim, Jong Min Kim, Donghun Han, Sang Soo Lee, Hyuck Mo 2024-01-19T08:03:55Z 2024-01-19T08:03:55Z 2023-09-21 2023-12 0926-3373 https://pubs.kist.re.kr/handle/201004/113072 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. English Elsevier BV Machine learning filters out efficient electrocatalysts in the massive ternary alloy space for fuel cells Article 10.1016/j.apcatb.2023.123128 1 Applied Catalysis B: Environmental, v.339 Applied Catalysis B: Environmental 339 N scie scopus 001060278200001 2-s2.0-85165880160 Chemistry, Physical Engineering, Environmental Engineering, Chemical Chemistry Engineering Article OXYGEN REDUCTION ACTIVITY NANOPARTICLES DISCOVERY PLATINUM STABILITY EVOLUTION CATALYST AG Fuel cells Electrocatalyst Ternary alloy Machine learning Catalyst design protocol