{
  "abstract": "Catalyst discovery for green hydrogen has optimized activity alone, deferring economic and environmental costs to stages where they disqualify otherwise promising candidates. Here we introduce a unified machine learning framework that screens catalytic activity and monetized carbon cost in a single pipeline. A machine learning potential predicts hydrogen evolution activity and stability at density-functional-theory accuracy. A second model, trained on published estimates of the social cost of carbon, converts each candidate's embodied CO2 into a monetary burden. Screening 460 graphene-supported dual-atom catalysts across 23 transition metals and 20 nitrogen-coordination motifs yields 26 active, synthesizable candidates. The two axes then conflict. The most active candidates span Mn-, Fe-, Zr-, Cr-, Cu-, Ni-, Ti-, Nb-, V-, Pd-, Au-, and Rh-based catalysts, indistinguishable in adsorption energy yet spanning a 58,000-fold range in carbon cost. Joint optimization selects Mn2@2Nf and Zr2@2Nc, matching the best precious-metal activity at tens-of-thousands-fold lower carbon cost, independent of catalyst loading.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2605.29821",
  "author_total": 11,
  "authors": [
    {
      "name": "Yanmei Zang"
    },
    {
      "name": "Hyun Gyu Park"
    },
    {
      "name": "Gi Beom Sim"
    },
    {
      "name": "Tae Hyeon Park"
    },
    {
      "name": "Ho Jin Lee"
    },
    {
      "name": "Xiaorong Zou"
    },
    {
      "name": "D. ChangMo Yang"
    },
    {
      "name": "Soohaeng Yoo Willow"
    },
    {
      "name": "Hye Jung Kim"
    },
    {
      "name": "Sunbin Yoo"
    },
    {
      "name": "Chang Woo Myung"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/1c9bdg1w4qh8f.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2605.29821",
  "fulltext_url": null,
  "id": "1c9bdg1w4qh8f",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2605.29821",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cond-mat.mtrl-sci"
    ],
    [
      "openalex",
      "2105|Electrocatalysts for Energy Conversion"
    ]
  ],
  "openalex": "W7162813051",
  "pdf_url": "https://arxiv.org/pdf/2605.29821",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-27",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/1c9bdg1w4qh8f.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:35:50Z",
      "native_id": "2605.29821",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2605.29821"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:cond-mat.mtrl-sci",
      "subfield": "condensed-matter"
    }
  ],
  "title": "Sustainability-Aware Discovery of Dual-Atom Hydrogen Evolution Catalysts via a Unified Machine Learning Framework for Activity and Carbon Cost",
  "title_text": "Sustainability-Aware Discovery of Dual-Atom Hydrogen Evolution Catalysts via a Unified Machine Learning Framework for Activity and Carbon Cost",
  "type": "preprint",
  "updated_at": "2026-10-30T12:35:50Z",
  "url": "https://research.draconitic.com/paper/1c9bdg1w4qh8f",
  "venue": "arXiv",
  "versions": []
}