{
  "abstract": "Electrolyte additive discovery remains challenging because experimentally validated molecules are sparse, whereas accessible chemical spaces are vast and largely unlabeled. This challenge is amplified in lithium-ion batteries, where additive performance arises from coupled interfacial reactions rather than a single molecular property. Here, we develop a prototype-guided molecular intelligence, ProtoMI, a literature-driven framework that learns transferable structural priors from reported electrolyte additives and uses them to prioritize candidates in unlabeled chemical space. For boron-containing additives, ProtoMI combines 126 literature-reported molecules with 179,977 unlabeled candidates. Graph contrastive learning identifies seven chemically interpretable prototypes from the reported additives, and prototype guided semi-supervised contrastive learning adapts these prototypes to the candidate space under source-target distribution mismatch. In retrospective temporal validation, ProtoMI achieves enrichment factors of 9.2-45.6 while screening less than 2% of the candidate space. A subsequent translation step identifies four commercially accessible candidates. One representative candidate, 4,4,5,5-Tetramethyl-2-[10-(1naphthyl)anthracen-9-yl]-1,3,2-dioxaborolane (TNDB), improves high-temperature LiFePO4||graphite cycling at 55 °C by 34.93% relative to the baseline electrolyte. An arsenal of characterizations and operando optical fiber Fourier transform infrared spectroscopy suggest that TNDB forms B-containing, F/P/O-modified inorganic interphases, suppresses solvent decomposition and reduces Fe deposition on graphite. This case study shows how sparse literature knowledge can guide experimentally efficient molecular discovery in data-scarce battery-additive spaces.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.02209",
  "author_total": 7,
  "authors": [
    {
      "name": "Weixiang Hong"
    },
    {
      "name": "Hongting Du"
    },
    {
      "name": "Jiayue Tang"
    },
    {
      "name": "Ruifeng Tan"
    },
    {
      "name": "Yangjian Quan"
    },
    {
      "name": "Jia Li"
    },
    {
      "name": "Jiaqiang Huang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/fax5s2zmhhtkk.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.02209",
  "fulltext_url": null,
  "id": "fax5s2zmhhtkk",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.02209",
  "language": null,
  "licence": "CC-BY-NC-SA-4.0",
  "licence_label": "CC BY-NC-SA 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by-nc-sa/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "physics.chem-ph"
    ],
    [
      "openalex",
      "2505|Machine Learning in Materials Science"
    ]
  ],
  "openalex": "W7207690370",
  "pdf_url": "https://arxiv.org/pdf/2609.02209",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-02",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/fax5s2zmhhtkk.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:16Z",
      "native_id": "2609.02209",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.02209"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "arxiv:physics.chem-ph",
      "subfield": "physical"
    }
  ],
  "title": "Prototype-guided transfer of sparse literature knowledge for electrolyte additive discovery",
  "title_text": "Prototype-guided transfer of sparse literature knowledge for electrolyte additive discovery",
  "type": "preprint",
  "updated_at": "2026-10-30T12:18:16Z",
  "url": "https://research.draconitic.com/paper/fax5s2zmhhtkk",
  "venue": "arXiv",
  "versions": []
}