{
  "abstract": "Machine learning (ML) has been increasingly applied to the ground-state properties of transition-metal complexes (TMCs), whereas its use for predicting their stateresolved photophysical properties remains comparatively limited. Here, we evaluate nine ML configurations: a descriptor-based XGBoost model and eight graph neural network (GNN) configurations, using excited-state reference data from tmQMg* together with molecular, structural, and electronic information from the tmQM dataset series. The models were trained to predict the 30 lowest vertical singlet excitations, including excitation energies (∆E), oscillator strengths ( f ), absorption properties, charge-transfer (CT) character, and the metal contributions to the occupied and virtual natural transition orbitals (NTOs). GNNs substantially outperformed XGBoost for ∆E. Among the investigated architectures, the polarizable atom interaction neural network (PaiNN) provided the most favorable balance between accuracy and input requirements, reproducing ∆E with a mean absolute error of 0.124 eV while requiring only an XYZ file and formal molecular charge. Furthermore, PaiNN reliably captured excited-state character, resulting in 87.0% accuracy for CT classification and occupied-and virtual-NTO metal-fraction MAEs of 0.082 and 0.058, respectively, indicating that it generally distinguishes metal-and ligand-centered orbital character well. Individual f values were more difficult to predict accurately; however, combining the predicted ∆E and intensities reproduced the principal features of the broadened absorption spectra. Overall, these results establish PaiNN as a fast and reliable predictor of multiple TMC excited-state properties from minimal molecular input, providing a practical route for large-scale photophysical screening and substantially reducing the need for time-dependent density functional theory (TD-DFT) calculations and subsequent excited-state analysis.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Chemistry",
    "University of Alberta",
    "Alberta Machine Intelligence Institute"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Ismael A. Elayan",
      "orcid": "0000-0002-7161-5155"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "J. Diego Castro-Miyashiro"
    },
    {
      "affs": [
        0,
        1,
        2
      ],
      "name": "J. Terence Blaskovits"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/ab6dtm6nfraw7.bib",
  "cited_by": 0,
  "doi": "10.26434/chemrxiv.15008673/v1",
  "fulltext_url": null,
  "id": "ab6dtm6nfraw7",
  "issns": [],
  "landing_url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15008673/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": "W7212291590",
  "pdf_url": "https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15008673/v1",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-10",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/ab6dtm6nfraw7.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:19Z",
      "native_id": "10.26434/chemrxiv.15008673",
      "source": "chemrxiv",
      "url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15008673/v1"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "openalex:2505|Machine Learning in Materials Science",
      "subfield": "materials-polymers"
    }
  ],
  "title": "Graph Neural Networks for Accelerated Prediction of Excited-State Properties in Transition-Metal Complexes",
  "title_text": "Graph Neural Networks for Accelerated Prediction of Excited-State Properties in Transition-Metal Complexes",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:19Z",
  "url": "https://research.draconitic.com/paper/ab6dtm6nfraw7",
  "venue": "ChemRxiv",
  "versions": []
}