{
  "abstract": "Aqueous stability is a fundamental requirement for materials used in electrochemical energy technologies, yet no systematic Pourbaix stability screening has been performed for Google DeepMind’s GNoME database of more than 520,000 novel inorganic crystals. Here, we introduce AQ-GNoME, an aqueous-stability augmentation of the GNoME database, enabling systematic shortlisting of GNoME candidates for electrochemical applications. The database additionally includes machine-learning-based substitutional-disorder predictions and Herfindahl–Hirschman Index supply-risk metrics, thereby supporting structural-order and supply-risk based filtering. User-defined criteria across electrochemical potential and pH, elemental composition, structural order, and supply concentration reduce the database to a manageable, project-specific shortlist. We apply AQ-GNoME to identify GNoME materials relevant for further investigation in electrochemical CO2 reduction (eCO2RR) and acidic oxygen evolution (OER), focusing on candidates absent from the Materials Project and ICSD. For eCO2RR, combining aqueous stability criteria with a compositional heuristic intended to mimic copper’s selectivity, we identify 8 ordered multimetallic candidates. For acidic OER, combining stability and supply-concentration criteria, we identify 39 Ir-lean oxides, of which 26 are metallic under HSE06 DFT. AQ-GNoME therefore provides a practical framework for electrochemical materials discovery within GNoME, reducing the search space from more than half a million compounds to experimentally tractable candidate sets.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Energy Conversion and Storage, Technical University of Denmark (DTU, Agnes Nielsens Vej, Kongens Lyngby, Denmark, 2800",
    "Department of Physics",
    "Technical University of Denmark (DTU"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 8,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Mathias S. Nissen"
    },
    {
      "affs": [
        0
      ],
      "name": "Peter Beck"
    },
    {
      "affs": [
        0
      ],
      "name": "Leo Karlsson"
    },
    {
      "affs": [
        0
      ],
      "name": "Manuel Dillenz"
    },
    {
      "affs": [
        1,
        2
      ],
      "name": "Alexander Bagger"
    },
    {
      "affs": [
        0
      ],
      "name": "Ivano E. Castelli"
    },
    {
      "affs": [
        0
      ],
      "name": "Heine A. Hansen"
    },
    {
      "name": "Heine Anton Hansen",
      "orcid": "0000-0001-7551-9470"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/e5qg9h93z95tr.bib",
  "cited_by": 0,
  "doi": "10.26434/chemrxiv.15003677/v2",
  "fulltext_url": null,
  "id": "e5qg9h93z95tr",
  "issns": [],
  "landing_url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15003677/v2",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2505|Machine Learning in Materials Science"
    ]
  ],
  "openalex": "W7213441729",
  "pdf_url": "https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15003677/v2",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-17",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/e5qg9h93z95tr.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:19Z",
      "native_id": "10.26434/chemrxiv.15003677",
      "source": "chemrxiv",
      "url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15003677/v2"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "openalex:2505|Machine Learning in Materials Science",
      "subfield": "materials-polymers"
    }
  ],
  "title": "AQ-GNoME: an Aqueous Stability Augmentation of the GNoME Database",
  "title_text": "AQ-GNoME: an Aqueous Stability Augmentation of the GNoME Database",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:19Z",
  "url": "https://research.draconitic.com/paper/e5qg9h93z95tr",
  "venue": "ChemRxiv",
  "versions": []
}