{
  "abstract": "Melt strength (MS) is a key parameter governing the processability and application of low-density polyethylene (LDPE), dictated by a complex interplay of molecular weight distribution (MWD), long-chain branching (LCB), and high-molecular-weight content—features poorly captured by conventional metrics like weight average molecular weight (Mw). This paper addresses the challenge by developing a physics-guided machine learning framework that constructs a list of molecular descriptors directly from full-spectrum MWD data using modern multi-detector high-temperature gel permeation chromatography (MD-GPC), explicitly aligned with the polymer entanglement fundamentals underlying melt strength. The descriptor enables accurate prediction and interpretable disentanglement of topological contributions. The paper further introduced a diffusion-based generative model for inverse design: given a target melt strength, the model proposed candidate molecular architectures and validated them through internal consistency with the learned structure–property relationships, offering a rational and data-driven strategy for the precise engineering of LDPE molecular structures, thus providing considerable flexibility for downstream engineering decisions. Practitioners may further refine or screen these candidates in light of real-world factors—including polymerization processing constraints, cost considerations for resin selection and blending, and manufacturing operation windows—thereby tailoring LDPE molecular structures/products to specific application requirements with high precision.",
  "abstract_shown": true,
  "affiliations": [
    "National Institute of Clean-and-Low-Carbon Energy, Beijing 102209, China"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 11,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Yifei Wang"
    },
    {
      "affs": [
        0
      ],
      "name": "Guogang Liu"
    },
    {
      "affs": [
        0
      ],
      "name": "Lin Liu"
    },
    {
      "affs": [
        0
      ],
      "name": "Yue Yu",
      "orcid": "0000-0002-7520-0547"
    },
    {
      "affs": [
        0
      ],
      "name": "Peiqian Yu",
      "orcid": "0009-0008-7317-1384"
    },
    {
      "affs": [
        0
      ],
      "name": "Linge Ma"
    },
    {
      "affs": [
        0
      ],
      "name": "Linlin Wei"
    },
    {
      "affs": [
        0
      ],
      "name": "Zhe Zhou"
    },
    {
      "affs": [
        0
      ],
      "name": "Linfeng Chen"
    },
    {
      "affs": [
        0
      ],
      "name": "Delin Yuan"
    },
    {
      "affs": [
        0
      ],
      "name": "Rongjuan Cong",
      "orcid": "0009-0000-6623-246X"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/7qdcxacnfhp42.bib",
  "cited_by": 0,
  "doi": "10.3390/polym18192421",
  "fulltext_url": null,
  "id": "7qdcxacnfhp42",
  "issns": [
    "2073-4360"
  ],
  "landing_url": "https://www.mdpi.com/2073-4360/18/19/2421",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2505|Machine Learning in Materials Science"
    ]
  ],
  "openalex": "W7220437424",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-04",
  "publisher": "MDPI AG",
  "ris": "https://research.draconitic.com/paper/7qdcxacnfhp42.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:44Z",
      "native_id": "10.3390/polym18192421",
      "source": "crossref",
      "url": "https://www.mdpi.com/2073-4360/18/19/2421"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "openalex:2505|Machine Learning in Materials Science",
      "subfield": "materials-polymers"
    }
  ],
  "title": "An Efficient Descriptor for Tubular LDPE Melt Strength via Physics-Guided Learning and Diffusion-Based Inverse Design",
  "title_text": "An Efficient Descriptor for Tubular LDPE Melt Strength via Physics-Guided Learning and Diffusion-Based Inverse Design",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:18:44Z",
  "url": "https://research.draconitic.com/paper/7qdcxacnfhp42",
  "venue": "Polymers",
  "versions": []
}