{
  "abstract": "Dopants can tune the activity of zirconia-based CO$_2$ hydrogenation catalysts through changes in adsorbate and transition-state (TS) energetics. Computing these quantities with density functional theory (DFT) becomes expensive when several dopants, reaction intermediates, and TSs must be screened. Here, we further develop the graph-based machine learning method Wasserstein Weisfeiler-Lehman Gaussian Process Regression (WWL-GPR): we introduce additional electronic-structure and local-environment features relevant for oxide surfaces and use the resulting model to predict adsorption and TS energies on ZrO$_2$(101) surfaces doped with Al$^{3+}$, Cd$^{2+}$, Ga$^{3+}$, In$^{3+}$, and Zn$^{2+}$. Adsorbate-surface structures are represented as attributed graphs that combine bonds connectivity and features. We benchmark WWL-GPR against thermodynamic scaling relations (TSR) for reaction intermediates and Bronsted-Evans-Polanyi relations (BEP) for TSs, and then propagate the predicted energies to microkinetic CH$_3$OH-yield estimates. WWL-GPR consistently improves the prediction of reaction intermediate energetics relative to linear scaling approaches and provides TS estimates that are competitive with BEP-based models. These improvements become especially relevant when the predicted energetics are propagated through the reaction network. Microkinetic calculations show that WWL-GPR-derived energies give CH$_3$OH yields closer to DFT-derived yields than the linear-relation workflow. Degree-of-rate-control analysis links the activity errors to dopant-dependent rate-controlling steps, including HCOO* formation, H$_2$COOH* dissociation, and H$_3$CO* formation. These results support WWL-GPR as a data-efficient surrogate for connecting atomistic energetics with catalyst-screening observables on doped oxide surfaces.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.13795",
  "author_total": 3,
  "authors": [
    {
      "name": "Karsten Walz Jensen"
    },
    {
      "name": "Mie Andersen"
    },
    {
      "name": "Raffaele Cheula"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/1nds4m8etaxcd.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.13795",
  "fulltext_url": null,
  "id": "1nds4m8etaxcd",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.13795",
  "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",
      "2505|Machine Learning in Materials Science"
    ]
  ],
  "openalex": "W7221575858",
  "pdf_url": "https://arxiv.org/pdf/2610.13795",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-09",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/1nds4m8etaxcd.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:28:36Z",
      "native_id": "2610.13795",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.13795"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:cond-mat.mtrl-sci",
      "subfield": "condensed-matter"
    }
  ],
  "title": "Improved screening of doped ZrO$_2$ catalysts for CO$_2$ hydrogenation from graph-based machine learning",
  "title_text": "Improved screening of doped ZrO_2 catalysts for CO_2 hydrogenation from graph-based machine learning",
  "type": "preprint",
  "updated_at": "2026-10-30T12:28:36Z",
  "url": "https://research.draconitic.com/paper/1nds4m8etaxcd",
  "venue": "arXiv",
  "versions": []
}