{
  "abstract": "Understanding and predicting the structure–property relationships in magnetic clusters remain challenging due to the complex interplay between geometry, electronic structure and spin configurations: density functional theory suffers from prohibitive computational cost for large-scale exploration, while existing graph neural networks struggle to balance long-range magnetic correlation capture, topological fidelity and scalability. We present MagCluster-GMAF, a physics-inspired hybrid graph architecture with magnetic cluster-aware serialization to preserve topological and electronic similarity during graph-to-sequence transformation, paired with a dual-branch design combining a Mamba-2 backbone for efficient long-range spin correlation modeling and a lightweight attention module as topological compensation. On a 2D doped magnetic material dataset, it achieved a total magnetic moment prediction MAE of 0.2683 μB, reducing the prediction error by 9.5% relative to the GPS + Transformer baseline and outperforming Graph-Mamba; at the 1400-node scale, it cut the GPU memory consumption by over 70% versus standard graph transformers while exhibiting near-linear empirical scaling at the studied graph scales. SHAP-based feature attribution analysis further identifies the key physicochemical descriptors governing the predictions, providing an effective approach for nanoscale magnetic material screening and the high-throughput screening of spintronic materials.",
  "abstract_shown": true,
  "affiliations": [
    "School of Artificial intelligence, Changchun Normal University, Changchun 130032, China",
    "iFLYTEK Co., Ltd., No. 666 Wangjiang West Road, Hefei 230088, China"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Yu Zhang"
    },
    {
      "affs": [
        0
      ],
      "name": "Jiale Wang"
    },
    {
      "affs": [
        0
      ],
      "name": "Haotian Liu"
    },
    {
      "affs": [
        0
      ],
      "name": "Yishuo Wang"
    },
    {
      "affs": [
        0
      ],
      "name": "Guangjie Liu"
    },
    {
      "affs": [
        1
      ],
      "name": "Wei Liu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/3d9fqmpc78gcz.bib",
  "cited_by": 0,
  "doi": "10.3390/nano16211422",
  "fulltext_url": null,
  "id": "3d9fqmpc78gcz",
  "issns": [
    "2079-4991"
  ],
  "landing_url": "https://www.mdpi.com/2079-4991/16/21/1422",
  "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": null,
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-04",
  "publisher": "MDPI AG",
  "ris": "https://research.draconitic.com/paper/3d9fqmpc78gcz.ris",
  "sources": [
    {
      "first_seen": "2026-11-05T00:20:54Z",
      "native_id": "10.3390/nano16211422",
      "source": "crossref",
      "url": "https://www.mdpi.com/2079-4991/16/21/1422"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "issn:2079-4991",
      "subfield": "materials-polymers"
    }
  ],
  "title": "MagCluster-GMAF: A Physics-Inspired Hybrid Graph Architecture for Long-Range Magnetic Property Prediction",
  "title_text": "MagCluster-GMAF: A Physics-Inspired Hybrid Graph Architecture for Long-Range Magnetic Property Prediction",
  "type": "peer-reviewed",
  "updated_at": "2026-11-06T00:21:10Z",
  "url": "https://research.draconitic.com/paper/3d9fqmpc78gcz",
  "venue": "Nanomaterials",
  "versions": []
}