{
  "abstract": "An attention‐based multimodal deep learning framework is developed to fuse processing parameters with microstructural micrographs for predicting creep rupture life of IN718 under a fixed creep testing condition. Under a predefined composition‐stratified, sample‐level split, the framework achieved a mean test‐set R2 of 0.917 ± 0.014 across 50 random‐seed training repetitions. The corresponding mean RMSE and MAPE were 0.14% and 6.0%, respectively. Interpretability analyses suggest that the predictions are consistent with established metallurgical understanding, particularly the important role of δ‐phase characteristics. Furthermore, uncertainty quantification endows the model with self‐assessment capabilities, allowing it to reliably quantify the confidence of its predictions. This study establishes a methodological framework that unifies predictive accuracy, physical interpretability, and model confidence, providing a validated paradigm for developing trustworthy AI models for materials design under data‐limited conditions.",
  "abstract_shown": true,
  "affiliations": [
    "Hebei Short Process Steelmaking Technology Innovation Center School of Materials Science and Engineering Hebei University of Science and Technology Shijiazhuang People's Republic of China",
    "National Engineering Research Center of Light Alloy Net Forming Shanghai Jiao Tong University Shanghai People's Republic of China",
    "State Key Laboratory for Advanced Metals and Materials University of Science and Technology Beijing Beijing People's Republic of China",
    "Hebei Lron and Steel Laboratory North China University of Science and Technology Tangshan China",
    "Yanzhao Lron and Steel Laboratory Tangshan People's Republic of China"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 11,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Haopeng Lv"
    },
    {
      "affs": [
        0
      ],
      "name": "Dayong Wu",
      "orcid": "0000-0002-6113-0904"
    },
    {
      "affs": [
        1
      ],
      "name": "Ziyuan Rao",
      "orcid": "0000-0003-1195-1779"
    },
    {
      "affs": [
        0
      ],
      "name": "Qian Wang"
    },
    {
      "affs": [
        0
      ],
      "name": "Haikun Ma"
    },
    {
      "affs": [
        0
      ],
      "name": "Jie Kang"
    },
    {
      "affs": [
        0
      ],
      "name": "Wang Li"
    },
    {
      "affs": [
        0
      ],
      "name": "Huicong Dong"
    },
    {
      "affs": [
        2
      ],
      "name": "Yandong Wang"
    },
    {
      "affs": [
        3,
        4
      ],
      "name": "Zhinan Yang"
    },
    {
      "affs": [
        0,
        2
      ],
      "name": "Ru Su"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/81jzy3ndsyb5n.bib",
  "cited_by": 0,
  "doi": "10.1002/advs.77026",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13455174/",
  "id": "81jzy3ndsyb5n",
  "issns": [
    "2198-3844"
  ],
  "landing_url": "https://doi.org/10.1002/advs.77026",
  "language": "en",
  "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": [
    [
      "heading",
      "Research Article"
    ],
    [
      "openalex",
      "2505|Machine Learning in Materials Science"
    ]
  ],
  "openalex": "W7202090240",
  "pdf_url": null,
  "pmcid": "PMC13455174",
  "pmid": "42573119",
  "published": "2026-08-10",
  "publisher": "Wiley",
  "ris": "https://research.draconitic.com/paper/81jzy3ndsyb5n.ris",
  "sources": [
    {
      "first_seen": "2026-11-06T12:23:10Z",
      "native_id": "PMC13455174",
      "source": "pmc",
      "url": "https://doi.org/10.1002/advs.77026"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "openalex:2505|Machine Learning in Materials Science",
      "subfield": "materials-polymers"
    }
  ],
  "title": "Trustworthy Multimodal Attention Framework for Creep Rupture Life Prediction Under Data‐Scarce Conditions: A Case Study on IN718",
  "title_text": "Trustworthy Multimodal Attention Framework for Creep Rupture Life Prediction Under Data‐Scarce Conditions: A Case Study on IN718",
  "type": "peer-reviewed",
  "updated_at": "2026-11-07T12:20:29Z",
  "url": "https://research.draconitic.com/paper/81jzy3ndsyb5n",
  "venue": "Advanced Science",
  "versions": []
}