{
  "abstract": "Advances in polymer synthesis and computational modeling are expanding the block-copolymer architecture space accessible for phase-behavior design. Machine learning can accelerate this exploration, yet a common representation capable of learning across diverse chain topologies remains lacking. Here, we present a graph neural network (GNN) representation of acyclic AB multiblock architectures, tested by random-phase approximation (RPA) instability prediction. Each chain is represented as a graph, enabling a common model to learn across diverse topologies. This representation, which organizes RPA-related information, retains predictive capability when extrapolated to architectures with more blocks and demonstrates its potential for reuse across tasks through transfer to instability-wavenumber prediction. By effectively linking block-copolymer chain architecture to field-theoretic response, this graph representation not only reconstructs stability-limit curves but also provides a practical route for future self-consistent field theory exploration of ordered phase space across complex topologies.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.20374",
  "author_total": 3,
  "authors": [
    {
      "name": "Xiyue Zhang"
    },
    {
      "name": "Yutong Wu"
    },
    {
      "name": "Yi-Xin Liu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/4phxwr0f9c8eq.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.20374",
  "fulltext_url": null,
  "id": "4phxwr0f9c8eq",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.20374",
  "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.soft"
    ],
    [
      "arxiv",
      "physics.comp-ph"
    ],
    [
      "openalex",
      "2505|Block Copolymer Self-Assembly"
    ]
  ],
  "openalex": "W7222327783",
  "pdf_url": "https://arxiv.org/pdf/2610.20374",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-14",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/4phxwr0f9c8eq.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:29:31Z",
      "native_id": "2610.20374",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.20374"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:physics.comp-ph",
      "subfield": "computational"
    },
    {
      "field": "physics",
      "origin": "arxiv:cond-mat.soft",
      "subfield": "condensed-matter"
    }
  ],
  "title": "Effective Graph Neural Representation of Chain Architectures of Block Copolymers Tested by Predicting RPA Instability Limits",
  "title_text": "Effective Graph Neural Representation of Chain Architectures of Block Copolymers Tested by Predicting RPA Instability Limits",
  "type": "preprint",
  "updated_at": "2026-10-30T12:29:31Z",
  "url": "https://research.draconitic.com/paper/4phxwr0f9c8eq",
  "venue": "arXiv",
  "versions": []
}