{
  "abstract": "Predicting the glass transition temperature (Tg) of polymers from structures remains difficult, and whether descriptors and molecular-language embeddings carry complementary information is unclear. CDEF-Tg (Compact Descriptor–Embedding Fusion for Glass Transition Temperature Prediction) concatenates 205 RDKit descriptors (RDKit205) with a frozen 384-dimensional ChemBERTa embedding (ChemBERTa384) and maps the 589-dimensional vector to Tg with an ExtraTreesRegressor. It was evaluated on 10,065 identity-resolved records of a polyimide-oriented dataset under three leakage-safe protocols: IID Group-Safe (IID, independent and identically distributed), Scaffold OOD (OOD, out-of-distribution), and Similarity OOD (T = 0.90). The model was selected on validation evidence and frozen before the final-test sets were opened. In the locked final test, CDEF-Tg reached a macro mean absolute error (MAE) of 48.84 °C, versus 50.17 °C (RDKit205 alone) and 54.33 °C (ChemBERTa384 alone). Group-aware bootstrap analyses separated CDEF-Tg from RDKit205 alone in MAE only under Similarity OOD and showed no MAE advantage over the comparator RDEF-Tg. Additional validation-only controls showed a lower macro MAE for a Morgan-fingerprint partner than for CDEF-Tg (49.11 versus 53.15 °C), and ChemBERTa384 lowered LightGBM error; the ExtraTrees ordering was stable across random states (split seeds were not varied). The results suggest a modest, protocol-dependent benefit within this dataset; the additional analyses provide descriptive evidence.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Computer Engineering, Faculty of Engineering and Architecture, Bingol University, 12000 Bingol, Türkiye",
    "Department of Computer Technologies, Bitlis Eren University, 13000 Bitlis, Türkiye"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Mücahit Çalışan",
      "orcid": "0000-0003-2651-5937"
    },
    {
      "affs": [
        1
      ],
      "name": "İbrahim Ayaz",
      "orcid": "0000-0003-3519-1882"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/7avf867sge0qd.bib",
  "cited_by": 0,
  "doi": "10.3390/polym18212783",
  "fulltext_url": null,
  "id": "7avf867sge0qd",
  "issns": [
    "2073-4360"
  ],
  "landing_url": "https://www.mdpi.com/2073-4360/18/21/2783",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [],
  "openalex": null,
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-07",
  "publisher": "MDPI AG",
  "ris": "https://research.draconitic.com/paper/7avf867sge0qd.ris",
  "sources": [
    {
      "first_seen": "2026-11-10T00:21:22Z",
      "native_id": "10.3390/polym18212783",
      "source": "crossref",
      "url": "https://www.mdpi.com/2073-4360/18/21/2783"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "issn:2073-4360",
      "subfield": "materials-polymers"
    }
  ],
  "title": "CDEF-Tg: Machine Learning-Based Descriptor–Embedding Fusion for Glass Transition Temperature Prediction in a Polyimide-Oriented Polymer Dataset",
  "title_text": "CDEF-Tg: Machine Learning-Based Descriptor–Embedding Fusion for Glass Transition Temperature Prediction in a Polyimide-Oriented Polymer Dataset",
  "type": "peer-reviewed",
  "updated_at": "2026-11-10T00:21:22Z",
  "url": "https://research.draconitic.com/paper/7avf867sge0qd",
  "venue": "Polymers",
  "versions": []
}