{
  "abstract": "Data-driven polymer discovery remains fragmented across databases, models, screening, literature, and simulations. We present PolyDiscover, a platform unifying multi-source property lookup, graph-neural-network prediction, trust-aware multi-objective screening, literature extraction, generative candidate exploration, and automated handoff to molecular-dynamics validation. Building on published ADEPT-PolyGraphMT, it harmonizes experimental, molecular-dynamics, density-functional-theory, and group-contribution data spanning 28 properties across about 13,000 real and one million virtual polymers. Its engine combines feasibility filtering and exact Pareto ranking with a trust score integrating similarity to known polymers, physical consistency, predictive uncertainty, and synthesizability, followed by diversity-aware shortlisting. Two case studies on lightweight, thermally stable structural materials and thermally stable electrical insulators, using monomer HOMO-LUMO gap as an insulation proxy, reduced a million-polymer search space to 30 chemically diverse candidates per task. Higher-fidelity molecular-dynamics and density-functional-theory calculations, interpreted after correcting systematic density underestimation by the molecular-dynamics protocol, showed retention of targeted property combinations. Ablation showed that trust-aware ranking shifted about one fifth of each shortlist toward more synthesizable chemistry closer to known polymers. A constrained natural-language interface translated a free-text request into a validated specification matching expert screening exactly, while staged literature evidence corroborated high glass-transition temperatures in polyimide-dominated shortlists against published experiments. PolyDiscover offers a traceable route from design intent to simulation-validated candidates, easing exploration of polymer chemical space.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.38843",
  "author_total": 6,
  "authors": [
    {
      "name": "Sobin Alosious"
    },
    {
      "name": "Guoyue Xu"
    },
    {
      "name": "Yuhan Liu"
    },
    {
      "name": "Renzheng Zhang"
    },
    {
      "name": "Meng Jiang"
    },
    {
      "name": "Tengfei Luo"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/7aggez5213w5h.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.38843",
  "fulltext_url": null,
  "id": "7aggez5213w5h",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.38843",
  "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",
      "physics.chem-ph"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2610.38843",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-28",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/7aggez5213w5h.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:36:23Z",
      "native_id": "2610.38843",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.38843"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "arxiv:physics.chem-ph",
      "subfield": "physical"
    }
  ],
  "title": "PolyDiscover: an integrated trust-aware platform for multi-objective polymer discovery and simulation validation",
  "title_text": "PolyDiscover: an integrated trust-aware platform for multi-objective polymer discovery and simulation validation",
  "type": "preprint",
  "updated_at": "2026-11-01T12:20:45Z",
  "url": "https://research.draconitic.com/paper/7aggez5213w5h",
  "venue": "arXiv",
  "versions": []
}