{
  "abstract": "Hydrogen activation and further hydrogenation of substrate cover chemical processes in synthetic and energetic-related technologies. In homogeneous catalysis, transition metal complexes with bifunctional nature offer a platform for the activation and later utilization of the hydrogen-source. However, the development of new catalysts is limited by long-term trial-and-error practices. Screening approaches offer the possibility of relying on key properties to identify promising candidates. Here, we present a data-driven workflow that integrates high-throughput chemical screening, quantum-mechanical (QM) calculations, and machine learning (ML) predictions to accelerate the rational discovery of promising bifunctional catalysts (BFC). Using the Ru-MACHO scaffold as a BFC reference, we systematically explore metal complex variations across four design axes: 11 metal centers, 2,538 pincer ligands, 74 transligands, and 3 axial ligands, generating ca. 1.5M candidate structures. QM-derived data and ML models predict catalyst hydrogenation thermodynamics, enabling rapid identification of top-performing candidates for further mechanistic validation in CO2 hydrogenation. This integrated approach delivers prioritized systems for experimental follow up, demonstrating how curated datasets, targeted QM calculations, and ML together streamline the discovery of next generation homogeneous catalysts for efficient hydrogenation/dehydrogenation reactions.",
  "abstract_shown": true,
  "affiliations": [
    "Hylleraas Centre for Quantum Molecular Sciences, Department of Chemistry, University of Oslo, P.O. Box 1033, Oslo, Norway, 0315",
    "Leibniz-Institut für Katalyse e.V., Albert-Einstein-Straße 29a, Rostock, Germany, 18059",
    "Centre for Materials Science and Nanotechnology, Department of Chemistry, University of Oslo, P.O. Box 1033, Oslo, Norway, 0315"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Lucía Morán-González"
    },
    {
      "affs": [
        0
      ],
      "name": "Hannes Kneiding"
    },
    {
      "affs": [
        0
      ],
      "name": "Mohamed E. A. Safy"
    },
    {
      "affs": [
        1
      ],
      "name": "Matthias Beller"
    },
    {
      "affs": [
        0
      ],
      "name": "David Balcells"
    },
    {
      "affs": [
        0,
        2
      ],
      "name": "Ainara Nova",
      "orcid": "0000-0003-3368-7702"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/dxzw48y29bxsj.bib",
  "cited_by": 0,
  "doi": "10.26434/chemrxiv.15008296/v1",
  "fulltext_url": null,
  "id": "dxzw48y29bxsj",
  "issns": [],
  "landing_url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15008296/v1",
  "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": "W7208711924",
  "pdf_url": "https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15008296/v1",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-04",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/dxzw48y29bxsj.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:17Z",
      "native_id": "10.26434/chemrxiv.15008296",
      "source": "chemrxiv",
      "url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15008296/v1"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "openalex:2505|Machine Learning in Materials Science",
      "subfield": "materials-polymers"
    }
  ],
  "title": "BiFuN: Machine Learning-Guided Bifunctional Catalysis in Hydrogenation Reactions",
  "title_text": "BiFuN: Machine Learning-Guided Bifunctional Catalysis in Hydrogenation Reactions",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:17Z",
  "url": "https://research.draconitic.com/paper/dxzw48y29bxsj",
  "venue": "ChemRxiv",
  "versions": []
}