{
  "abstract": "Hybrid machine learning/molecular mechanics (ML/MM) methods have emerged as a powerful approach to multiscale modelling. Recent advances have focused on electrostatic embedding schemes, in which atoms in the ML region are polarized by the surrounding MM environment. Consequently, ML/MM electrostatics have been the subject of extensive study, whereas dispersion and close-range repulsive interactions remain comparatively underexplored. In analogy to established QM/MM practice, exchange-repulsion and dispersion interactions are commonly described using Lennard-Jones (LJ) 12-6 potentials with parameters transferred from existing force field libraries. However, such transferable parameters may be suboptimal for ML/MM interfaces, where consistency with the electrostatic embedding model is desirable. Here, we develop methodologies for deriving dynamic LJ parameters based on the exchange-hole dipole moment (XDM) model and the Fedorov polarizability scaling relation. Moreover, we propose a one-shot procedure for fine-tuning LJ parameters to experimental free energies. Focusing on the electrostatic machine learning embedding (EMLE) scheme, we show that these approaches yield EMLE models with competitive accuracy relative to conventional force fields and mechanical embedding schemes, while additionally providing chemically environment-aware LJ parameters. This capability offers a route to improved accuracy in demanding applications such as reactive modelling, where dispersion coefficients may depend strongly on the local chemical environment.",
  "abstract_shown": true,
  "affiliations": [
    "EaStCHEM School of Chemistry",
    "University of Edinburgh",
    "Departament de Química Física",
    "Universitat de València",
    "OpenBioSim Community Interest Company",
    "School of Natural and Environmental Sciences",
    "Newcastle University"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "João Morado",
      "orcid": "0000-0002-5003-2233"
    },
    {
      "affs": [
        2,
        3
      ],
      "name": "Kirill Zinovjev"
    },
    {
      "affs": [
        4
      ],
      "name": "Lester O. Hedges"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Julien Michel"
    },
    {
      "affs": [
        5,
        6
      ],
      "name": "Daniel J. Cole"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/7kt607dgabyfw.bib",
  "cited_by": 0,
  "doi": "10.26434/chemrxiv.15004404/v2",
  "fulltext_url": null,
  "id": "7kt607dgabyfw",
  "issns": [],
  "landing_url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15004404/v2",
  "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",
      "3107|Advanced Chemical Physics Studies"
    ]
  ],
  "openalex": "W7213986935",
  "pdf_url": "https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15004404/v2",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-22",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/7kt607dgabyfw.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:21Z",
      "native_id": "10.26434/chemrxiv.15004404",
      "source": "chemrxiv",
      "url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15004404/v2"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "openalex:3107|Advanced Chemical Physics Studies",
      "subfield": "amo"
    }
  ],
  "title": "Atomic Environment Aware Lennard-Jones Parameterization for Electrostatic Embedding ML/MM Simulations",
  "title_text": "Atomic Environment Aware Lennard-Jones Parameterization for Electrostatic Embedding ML/MM Simulations",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:21Z",
  "url": "https://research.draconitic.com/paper/7kt607dgabyfw",
  "venue": "ChemRxiv",
  "versions": []
}