{
  "abstract": "The singlet–triplet gap (STG) is an important property of photofunctional materials, but obtaining reliable values can be challenging. Experimentally, its determination often requires a combination of advanced spectroscopic techniques, while computational approaches can be time-consuming and demand specialized expertise. Therefore, a low-cost and user-friendly approach for obtaining reasonable STG estimates could make this property more accessible to non-experts. In this work, we evaluate regression models based on different molecular representations using the INVEST dataset from the Aspuru-Guzik group that contains ADC(2) energy gaps for ~70K molecules. We compare transformer models operating on SMILES representations, graph-based models (AttentiveFP), and fingerprint-based models. While AttentiveFP performs best for molecules similar to those in the training set, pre-trained transformers generalize better to unseen chemical space after fine-tuning. Motivated by these complementary strengths, we integrate our best-performing models into an online 2D chemical structure editor, enabling users to obtain STG estimates directly from drawn molecular structures. The editor can be accessed at http://ketcher-predict.ttk.hu/.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "name": "Péter Pál Fehér"
    },
    {
      "name": "András Stirling"
    },
    {
      "name": "Andras Stirling",
      "orcid": "0000-0002-1696-7932"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/4454ny2a55vpx.bib",
  "cited_by": 0,
  "doi": "10.26434/chemrxiv.15008084/v1",
  "fulltext_url": null,
  "id": "4454ny2a55vpx",
  "issns": [],
  "landing_url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15008084/v1",
  "language": null,
  "licence": "CC-BY-NC",
  "licence_label": "CC BY-NC",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by-nc/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2505|Machine Learning in Materials Science"
    ]
  ],
  "openalex": "W7204790389",
  "pdf_url": "https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15008084/v1",
  "pmcid": null,
  "pmid": null,
  "published": "2026-08-31",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/4454ny2a55vpx.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:17Z",
      "native_id": "10.26434/chemrxiv.15008084",
      "source": "chemrxiv",
      "url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15008084/v2"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "openalex:2505|Machine Learning in Materials Science",
      "subfield": "materials-polymers"
    }
  ],
  "title": "Prediction of Singlet-Triplet Gaps Using Self-Attention–Based Regression with Model Integration into a Molecular Editor",
  "title_text": "Prediction of Singlet-Triplet Gaps Using Self-Attention–Based Regression with Model Integration into a Molecular Editor",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:24Z",
  "url": "https://research.draconitic.com/paper/4454ny2a55vpx",
  "venue": "ChemRxiv",
  "versions": []
}