{
  "abstract": "Modeling the electric field gradient (EFG) in molecular dynamics (MD) simulations enables the prediction of nuclear magnetic resonance (NMR) relaxation rates thus allowing to unravel the underlying relaxation-active molecular motion. Here, we compare the linear-response Sternheimer model with a novel machine-learning (ML) approach to simulate 14 N quadrupolar T 1 relaxation in the ionic liquid 1-butyl-3-methylimidazolium tetrafluoroborate ([BMIM][BF 4 ]). Improving the parametrization of the Sternheimer model at the low-symmetry nitrogen sites required an electron density-based charge-partitioning scheme. In contrast, the parameter-free ML descriptor directly captured the EFG dynamics, providing a flexible methodology for computing EFG tensors in complex ionic systems. In the extreme-narrowing regime, both models predicted relaxation rates in excellent agreement with experimental measurements. Finally, using a regularized inverse Laplace transform, we deconvolute the dynamics of the EFG tensor and of the cation inertia tensor, and identify molecular rotation as the dominant mode responsible for 14 N quadrupolar relaxation.",
  "abstract_shown": true,
  "affiliations": [
    "Theory Department, Fritz Haber Institute of the Max Planck Society, Faradayweg 4–6, Berlin, Germany, 14195",
    "Department of Chemistry",
    "New York University",
    "ELI-NP",
    "Horia Hulubei National Institute for Physics and Nuclear Engineering",
    "Institute of Energy Technologies, Fundamental Electrochemistry (IET-1), Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Straße, Jülich, Germany, 52425",
    "Institute of Technical and Macromolecular Chemistry, RWTH Aachen University, Worringerweg 2, Aachen, Germany, 52074"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 7,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Federico Civaia"
    },
    {
      "affs": [
        1,
        2
      ],
      "name": "Yanan Li"
    },
    {
      "affs": [
        1,
        2,
        3,
        4
      ],
      "name": "Florin Teleanu"
    },
    {
      "affs": [
        0,
        5
      ],
      "name": "Simone S. Köcher"
    },
    {
      "affs": [
        5,
        6
      ],
      "name": "Josef Granwehr"
    },
    {
      "affs": [
        1,
        2
      ],
      "name": "Alexej Jerschow"
    },
    {
      "affs": [
        0,
        5
      ],
      "name": "Christoph Scheurer"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/6zhyvw8yh1ewy.bib",
  "cited_by": 0,
  "doi": "10.26434/chemrxiv.15008848/v1",
  "fulltext_url": null,
  "id": "6zhyvw8yh1ewy",
  "issns": [],
  "landing_url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15008848/v1",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1607|Advanced NMR Techniques and Applications"
    ]
  ],
  "openalex": "W7213247200",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-15",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/6zhyvw8yh1ewy.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:19Z",
      "native_id": "10.26434/chemrxiv.15008848",
      "source": "chemrxiv",
      "url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15008848/v1"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "openalex:1607|Advanced NMR Techniques and Applications",
      "subfield": "physical"
    }
  ],
  "title": "Machine-Learning Models Bypass Charge Computation for Modeling 14N Quadrupolar Relaxation in Ionic Liquids",
  "title_text": "Machine-Learning Models Bypass Charge Computation for Modeling 14N Quadrupolar Relaxation in Ionic Liquids",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:19Z",
  "url": "https://research.draconitic.com/paper/6zhyvw8yh1ewy",
  "venue": "ChemRxiv",
  "versions": []
}