{
  "abstract": "Poly(1,3-dioxolane) (pDXL) is a chemically recyclable polyether that can be synthesized by living cationic ring-opening polymerization with precise control of chain length into the ultra-high-molecular-weight (UHMW) regime, where it acquires enhanced mechanical properties. Like most polymers, however, it has amorphous condensed-phase structure, and diffraction measurements alone cannot resolve the atomistic microstructure that underlies them. In this work, we predict the ensemble of the pDXL microstructures by applying a recently developed AI-accelerated ab initio bottom-up polymer structure prediction (AI$^{2}$-BPSP) framework that simulates the living polymerization of pDXL under experimental synthetic conditions with machine-learning force fields trained within van der Waals-corrected hybrid density functional theory. Partitioning the predicted X-ray and neutron diffraction signal between the growing chain and the surrounding monomer resolves its dominant feature into two counteracting modes: a monomer contribution near $q \\approx 1.5$ Å$^{-1}$ that decays as monomer is consumed, and a polymer contribution near $q \\approx 1.6$ Å$^{-1}$ that is absent in oligomers and grows with chain length. This work paves the way to direct accuracy validation of these statistically significant, first-principles, and chain-length resolved diffraction predictions against experiments.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.09388",
  "author_total": 6,
  "authors": [
    {
      "name": "Yizhi Song"
    },
    {
      "name": "Bharatha K. Gunawardana"
    },
    {
      "name": "Kriti Alam"
    },
    {
      "name": "Teresa Shah"
    },
    {
      "name": "Deepa Ranabhat"
    },
    {
      "name": "Hsin-Yu Ko"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/6cr6h1e7z01j2.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.09388",
  "fulltext_url": null,
  "id": "6cr6h1e7z01j2",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.09388",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cond-mat.dis-nn"
    ],
    [
      "arxiv",
      "cond-mat.mtrl-sci"
    ],
    [
      "arxiv",
      "physics.chem-ph"
    ],
    [
      "openalex",
      "2505|Machine Learning in Materials Science"
    ]
  ],
  "openalex": "W7220996107",
  "pdf_url": "https://arxiv.org/pdf/2610.09388",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-07",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/6cr6h1e7z01j2.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:27:37Z",
      "native_id": "2610.09388",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.09388"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "arxiv:physics.chem-ph",
      "subfield": "physical"
    },
    {
      "field": "physics",
      "origin": "arxiv:cond-mat.dis-nn",
      "subfield": "condensed-matter"
    }
  ],
  "title": "Bottom-Up Prediction of Amorphous Poly(1,3-dioxolane) using Ab Initio Reactive Machine-Learning Force Fields",
  "title_text": "Bottom-Up Prediction of Amorphous Poly(1,3-dioxolane) using Ab Initio Reactive Machine-Learning Force Fields",
  "type": "preprint",
  "updated_at": "2026-10-30T12:27:37Z",
  "url": "https://research.draconitic.com/paper/6cr6h1e7z01j2",
  "venue": "arXiv",
  "versions": []
}