{
  "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.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.30133",
  "author_total": 22,
  "authors": [
    {
      "name": "Ken J. Jenewein"
    },
    {
      "name": "Faezeh Habib Zadeh"
    },
    {
      "name": "Xiaoxiao Wang"
    },
    {
      "name": "Gustavo Malkomes"
    },
    {
      "name": "Huafan Zhang"
    },
    {
      "name": "Natalie Page"
    },
    {
      "name": "Jae Jin Bang"
    },
    {
      "name": "Peter J. Santiago"
    },
    {
      "name": "Karla V. Contreras"
    },
    {
      "name": "Katherine K. Li"
    },
    {
      "name": "Allison Perna"
    },
    {
      "name": "Lorena M. Britton"
    },
    {
      "name": "Fahrettin Kilic"
    },
    {
      "name": "Kevin J. Cruse"
    },
    {
      "name": "Armin Taheri"
    },
    {
      "name": "Krishnanand Mallayya"
    },
    {
      "name": "Harley Quinn"
    },
    {
      "name": "Rebecca A. Durr"
    },
    {
      "name": "Peter A. Beaucage"
    },
    {
      "name": "Santiago Miret"
    },
    {
      "name": "John M. Gregoire"
    },
    {
      "name": "Rafael Gómez-Bombarelli"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/a1tssavxd1ga0.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.30133",
  "fulltext_url": null,
  "id": "a1tssavxd1ga0",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.30133",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cond-mat.mtrl-sci"
    ],
    [
      "openalex",
      "2105|Electrocatalysts for Energy Conversion"
    ]
  ],
  "openalex": "W7214395058",
  "pdf_url": "https://arxiv.org/pdf/2609.30133",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-25",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/a1tssavxd1ga0.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:24:52Z",
      "native_id": "2609.30133",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.30133"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:cond-mat.mtrl-sci",
      "subfield": "condensed-matter"
    }
  ],
  "title": "AI-guided high-throughput discovery of iridium- and ruthenium-free palladium-oxide catalysts for durable acidic oxygen evolution",
  "title_text": "AI-guided high-throughput discovery of iridium- and ruthenium-free palladium-oxide catalysts for durable acidic oxygen evolution",
  "type": "preprint",
  "updated_at": "2026-10-30T12:24:52Z",
  "url": "https://research.draconitic.com/paper/a1tssavxd1ga0",
  "venue": "arXiv",
  "versions": []
}