{
  "abstract": "Designing efficient degradation pathways for per- and polyfluoroalkyl substances (PFAS) requires access to accurate bond dissociation energies (BDEs). Composite quantum chemical methods such as G4 provide high accuracy (<1 kcal mol–1) but are limited to small molecules. Density functional theory (DFT) can handle larger systems, but its results vary significantly with the choice of functional and basis set, raising concerns about reliability. Fragment-based methods like the connectivity-based hierarchy (CBH) and its modified form (mCBH) offer G4-level accuracy at DFT cost, but only when using large fragments. Since obtaining G4 energies for large fragments is computationally demanding, these methods are not practical for building large BDE databases. To address this limitation, we introduce a machine learning (ML)-enhanced CBH approach. Here, a regression model is trained on G4-calculated energies of small fragments and is used to predict PFAS BDEs. This strategy achieves near-G4 accuracy without requiring G4 calculations on large fragments. Across diverse PFAS with varying chain lengths and functional groups, our ML-augmented fragmentation-based methods either match or outperform the accuracy of both the mCBH-3 and CBH-3 approaches (both of which require larger fragments), demonstrating that chemical accuracy can be achieved with smaller fragments. By integrating fragment-level quantum data with ML, this hybrid framework provides an efficient pathway for generating reliable BDE datasets and accelerating the discovery of effective PFAS degradation strategies.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Chemistry",
    "Indian Institute of Technology Madras",
    "Center for Quantum Information, Communication, and Computing",
    "Center for Atomistic Modeling and Materials Design",
    "Department of Metallurgical and Materials Engineering",
    "Center for Molecular Materials and Functions"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0,
        1,
        2
      ],
      "name": "Kalpak Ghosh"
    },
    {
      "affs": [
        0,
        1,
        3
      ],
      "name": "Samir Kumar Nayak"
    },
    {
      "affs": [
        3,
        1,
        4
      ],
      "name": "Rohit Batra"
    },
    {
      "affs": [
        0,
        1,
        2,
        3,
        5
      ],
      "name": "Sharma S. R. K. C. Yamijala",
      "orcid": "0000-0003-1773-9226"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/a59nsb1b9pxj0.bib",
  "cited_by": 0,
  "doi": "10.26434/chemrxiv.15009576/v1",
  "fulltext_url": null,
  "id": "a59nsb1b9pxj0",
  "issns": [],
  "landing_url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15009576/v1",
  "language": null,
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "3107|Advanced Chemical Physics Studies"
    ]
  ],
  "openalex": "W7214752624",
  "pdf_url": "https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15009576/v1",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-28",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/a59nsb1b9pxj0.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:21Z",
      "native_id": "10.26434/chemrxiv.15009576",
      "source": "chemrxiv",
      "url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15009576/v1"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "openalex:3107|Advanced Chemical Physics Studies",
      "subfield": "amo"
    }
  ],
  "title": "Harnessing Machine Learning and Composite Quantum Chemical Methods for Computing Accurate Bond Dissociation Energies of PFAS",
  "title_text": "Harnessing Machine Learning and Composite Quantum Chemical Methods for Computing Accurate Bond Dissociation Energies of PFAS",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:21Z",
  "url": "https://research.draconitic.com/paper/a59nsb1b9pxj0",
  "venue": "ChemRxiv",
  "versions": []
}