AI 引导高通量发现无铱无钌钯氧化物催化剂，用于耐久酸性析氧

Condensed Matter > Materials Science arXiv:2609.30133 (cond-mat) [Submitted on 24 Sep 2026] Title:AI-guided high-throughput discovery of iridium- and ruthenium-free palladium-oxide catalysts for durable acidic oxygen evolution Authors:Ken J. Jenewein, Faezeh Habib Zadeh, Xiaoxiao Wang, Gustavo Malkomes, Huafan Zhang, Natalie Page, Jae Jin Bang, Peter J. Santiago, Karla V. Contreras, Katherine K. Li, Allison Perna, Lorena M. Britton, Fahrettin Kilic, Kevin J. Cruse, Armin Taheri, Krishnanand Mallayya, Harley Quinn, Rebecca A. Durr, Peter A. Beaucage, John M. Gregoire, Rafael Gómez-Bombarelli View a PDF of the paper titled AI-guided high-throughput discovery of iridium- and ruthenium-free palladium-oxide catalysts for durable acidic oxygen evolution, by Ken J. Jenewein and 20 other authors View PDF Abstract:Catalyzing acidic oxygen evolution at the proton-exchange-membrane water electrolysis (PEMWE) anode relies almost entirely on iridium or ruthenium, drawn from concentrated supply chains that constrain gigawatt-scale deployment. We report an artificial intelligence (AI)-guided, human-supervised closed-loop platform (>90% automation) integrating combinatorial sputter synthesis, high-throughput screening, machine-learning composition-property models, adaptive multi-objective optimization, and context-aware large-language-model reasoning, where lead catalysts advanced to long-term validation in 1 M H2SO4 at 10 mA cm-2. Navigating a combinatorial metal oxide space, the platform iteratively evaluated the activity-stability trade-off of 2,942 catalysts across 53 material systems and 26 elements, surfacing Ir- and Ru-free complex oxides such as InMnPdOx and NiTaPdOx that conventional design logic, and off-the-shelf language models, would not predict. In retrospective benchmarking, our sequential learning agent advanced the activity-stability frontier faster than fixed-policy Bayesian optimization or in-context language-model selection. During long-term testing, NiTaPdOx operated at lower overpotential than PdOx, but both eventually exceeded 0.5 V: PdOx at ~200 h and NiTaPdOx at ~470 h. InMnPdOx showed a similar overpotential improvement in addition to a dramatic increase in operational stability, retaining overpotential below 0.5 V over 1,000 h of operation. The additive elements promote the formation of a nanostructure that is associated with catalytic activity while stabilizing Pd against corrosion. The results highlight the power of AI-driven science in addressing long-standing challenges in materials chemistry, and the greater availability of Pd relative to incumbent Ir and Ru offers a near-term option to ease supply constraints on scaled electrochemical H2 generation. Subjects: Materials Science (cond-mat.mtrl-sci) Cite as: arXiv:2609.30133 [cond-mat.mtrl-sci] (or arXiv:2609.30133v1 [cond-mat.mtrl-sci] for this version) https://doi.org/10.48550/arXiv.2609.30133 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Ken Jenewein [view email] [v1] Thu, 24 Sep 2026 17:04:37 UTC (11,345 KB) Full-text links: Access Paper: View a PDF of the paper titled AI-guided high-throughput discovery of iridium- and ruthenium-free palladium-oxide catalysts for durable acidic oxygen evolution, by Ken J. 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