{
  "abstract": "Low-temperature operation imposes severe limitations on lithium-ion transport in battery electrolytes, yet the coupled effects of solvent composition and fluorinated additives in the cold-temperature regime remain insufficiently resolved. Here, we combine classical molecular dynamics (MD) and machine learning (ML) to investigate 1 M LiPF$_6$ electrolytes containing ethylene carbonate (EC), ethyl methyl carbonate (EMC), and EC/EMC (3:7), with 0-10 mol% fluoroethylene carbonate (FEC), from 298 to 233 K. MD simulations quantify Li$^+$ self-diffusion, Nernst-Einstein (NE) and Green-Kubo (GK) conductivities and local coordination, while Gaussian-process surrogates model conductivity across composition and temperature. Cooling produces a pronounced transport penalty, particularly in EMC-containing electrolytes, whose GK conductivity decreases by more than 98% at 233 K, compared with approximately 90% in EC-rich systems. Li$^+$ self-diffusion activation energies are 0.49-0.54 eV for EMC-containing systems and 0.27-0.29 eV for EC-based systems. Within the EC family, 0-2 mol% FEC gives comparable cold-temperature transport, whereas 5-10 mol% FEC shows lower conductivity retention at the coldest simulated temperature. The analyzed coordination channels remain solvent dominated, while direct Li$^+$-FEC coordination is not quantified in the present RDF set.The Gaussian-process surrogates achieve composition-disjoint cross-validated RMSE values of 0.56 and 0.57 mS cm$^{-1}$ for NE and GK conductivity. Within the simulated liquid-state trajectories, temperature and host-solvent composition dominate the bulk-transport response, with FEC acting as a secondary modifier.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.02824",
  "author_total": 4,
  "authors": [
    {
      "name": "İpek Yenda Çınar"
    },
    {
      "name": "Oguzhan Orhan"
    },
    {
      "name": "M. Oluş Özbek"
    },
    {
      "name": "Şener Özönder"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/2xj84w1xcsfem.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.02824",
  "fulltext_url": null,
  "id": "2xj84w1xcsfem",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.02824",
  "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": null,
  "pdf_url": "https://arxiv.org/pdf/2609.02824",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-02",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/2xj84w1xcsfem.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:16Z",
      "native_id": "2609.02824",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.02824"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "arxiv:physics.chem-ph",
      "subfield": "physical"
    },
    {
      "field": "physics",
      "origin": "arxiv:cond-mat.dis-nn",
      "subfield": "condensed-matter"
    }
  ],
  "title": "Low-Temperature Transport in Li-Ion Battery EC/EMC/FEC Electrolytes: Molecular Dynamics and Machine-Learning Modeling",
  "title_text": "Low-Temperature Transport in Li-Ion Battery EC/EMC/FEC Electrolytes: Molecular Dynamics and Machine-Learning Modeling",
  "type": "preprint",
  "updated_at": "2026-10-30T12:18:16Z",
  "url": "https://research.draconitic.com/paper/2xj84w1xcsfem",
  "venue": "arXiv",
  "versions": []
}