{
  "abstract": "Solid-state 13C nuclear magnetic resonance (NMR) spectroscopy is among the most powerful probes of polymer structure, yet predicting solid-state 13C NMR spectra from molecular simulations has remained infeasible due to intractable computational costs. Here we introduce PNGWNN, a framework coupling molecular dynamics and machinelearned chemical shifts to efficiently generate solid-state 13C NMR spectra from molecular simulation. Trained on three polymer chemistries, PNGWNN achieves ∼1 ppm chemical shift prediction accuracy against density functional theory and transfers quantitatively to six chemically distinct test polymers across amorphous, crystalline, surface, multiphase, and copolymer morphologies. Moreover, predicted spectral features can be decomposed into atomically detailed contributions associated with specific conformations, hydrogen-bond motifs, and phase compositions, revealing structure–spectrum relationships without prior structural hypotheses. PNGWNN makes solid-state 13C NMR a computationally accessible observable, opening routes to simulation-driven structural inference and experimental design in polymers.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "name": "Hang Zhang"
    },
    {
      "name": "Nicholas E. Jackson",
      "orcid": "0000-0002-1470-1903"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/echgvn043gnwf.bib",
  "cited_by": 0,
  "doi": "10.26434/chemrxiv.15001687/v2",
  "fulltext_url": null,
  "id": "echgvn043gnwf",
  "issns": [],
  "landing_url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15001687/v2",
  "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",
      "2505|Machine Learning in Materials Science"
    ]
  ],
  "openalex": "W7211918013",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-06",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/echgvn043gnwf.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:17Z",
      "native_id": "10.26434/chemrxiv.15001687",
      "source": "chemrxiv",
      "url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15001687/v2"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "openalex:2505|Machine Learning in Materials Science",
      "subfield": "materials-polymers"
    }
  ],
  "title": "Machine-Learned Solid-State 13C Chemical Shifts for Structure-Resolved Analysis of Polymer Simulations",
  "title_text": "Machine-Learned Solid-State 13C Chemical Shifts for Structure-Resolved Analysis of Polymer Simulations",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:17Z",
  "url": "https://research.draconitic.com/paper/echgvn043gnwf",
  "venue": "ChemRxiv",
  "versions": []
}