{
  "abstract": "A continuing challenge in aerospace materials is the search for alloys that have desired functional properties to operate at higher temperatures with lower densities. This improves aero‐engine efficiency and reduces CO 2 and other harmful emissions, aligning with aviation industry targets for emission reduction. Nb─Si alloys can operate at higher temperatures than current Ni‐based superalloys and have lower densities. We develop a machine learning‐driven design framework for Nb─Si based ultra‐high temperature alloys using a three‐step feature screening strategy to break the 20 MPa·m 1/2 fracture toughness barrier. Our model predicts fracture toughness ( K Q ) with an error <7%, and we use SHAP(ley) and PDP analysis to interpret the model to guide alloy design. Five alloys with Si content ranging from 3 to 15 at.% were synthesized to validate model predictions. Sample #5 (Nb 38.5 Ti 38.5 Si 3 Zr 18 V 2 ) achieved a K Q of 22.791 MPa·m 1/2 , exceeding the typical range (below 20 MPa·m 1/2 ) for as‐cast Nb─Si alloys. Microstructural analysis showed that the improved performance resulted from the transformation of brittle silicide phases to ductile Nbss phase and crack‐bridging toughening. Strengthening mechanism analysis reveals that solid solution strengthening was dominant (68%–84%), with excellent strength‐toughness balance.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Mechanical Manufacturing and Biomedical Engineering Trinity College University of Dublin Dublin Ireland",
    "National Key Laboratory For Precision Hot Processing of Metals Harbin Institute of Technology Harbin P. R. China",
    "AiMaterials Research LLC Santa Fe New Mexico USA",
    "State Key Laboratory For Mechanical Behavior of Materials State Key Laboratory of Porous Metal Materials School of Materials Science and Engineering Xi'an Jiatong University Xi'an P. R. China"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 8,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Dezhi Chen",
      "orcid": "0009-0000-9246-4141"
    },
    {
      "affs": [
        1
      ],
      "name": "Chao Xu",
      "orcid": "0000-0002-5099-0060"
    },
    {
      "affs": [
        1
      ],
      "name": "Jingyue Yu"
    },
    {
      "affs": [
        1
      ],
      "name": "Qi Ming Wang",
      "orcid": "0000-0002-4207-189X"
    },
    {
      "affs": [
        1
      ],
      "name": "Hongze Fang",
      "orcid": "0000-0002-0761-2478"
    },
    {
      "affs": [
        0
      ],
      "name": "Shuo Yin",
      "orcid": "0000-0001-7893-8814"
    },
    {
      "affs": [
        2,
        3
      ],
      "name": "Turab Lookman",
      "orcid": "0000-0001-8122-5671"
    },
    {
      "affs": [
        1
      ],
      "name": "Ruirun Chen",
      "orcid": "0000-0003-3695-997X"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/ctkrk31v39703.bib",
  "cited_by": 21,
  "doi": "10.1002/advs.75815",
  "fulltext_url": null,
  "id": "ctkrk31v39703",
  "issns": [
    "2198-3844"
  ],
  "landing_url": "https://doi.org/10.1002/advs.75815",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2505|Machine Learning in Materials Science"
    ]
  ],
  "openalex": "W7162343058",
  "pdf_url": null,
  "pmcid": "PMC13336009",
  "pmid": "42179023",
  "published": "2026-05-25",
  "publisher": "Wiley",
  "ris": "https://research.draconitic.com/paper/ctkrk31v39703.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:27:00Z",
      "native_id": "W7162343058",
      "source": "openalex",
      "url": "https://doi.org/10.1002/advs.75815"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "openalex:2505|Machine Learning in Materials Science",
      "subfield": "materials-polymers"
    }
  ],
  "title": "Interpretable Machine Learning Framework for Nb─Si Based Alloy Design with Enhanced Fracture Toughness",
  "title_text": "Interpretable Machine Learning Framework for Nb─Si Based Alloy Design with Enhanced Fracture Toughness",
  "type": "peer-reviewed",
  "updated_at": "2026-11-06T13:20:44Z",
  "url": "https://research.draconitic.com/paper/ctkrk31v39703",
  "venue": "Advanced Science",
  "versions": []
}