{
  "abstract": "High temperature Nb-Si based alloys face a critical challenge: achieving adequate room-temperature fracture toughness ( > 18 MPa·m 1/2 ) for processing while maintaining high-temperature strength, properties that typically compete with each other. Here, we overcome this inherent trade-off through machine learning-guided alloy design, employing a three-step feature screening strategy to identify 6 key descriptors from 200 initial features. SHAP analysis reveals how melting enthalpy and atomic radius mismatch control property outcomes, enabling targeted multi-objective optimization via NSGA-II algorithm. The optimized Nb-12.26Si-21.35Ti-1.98Al-1.96Cr-0.51Hf-4.34Zr-4.35 V alloy achieves an as-cast fracture toughness of 18.92 MPa·m 1/2 while maintaining 322 MPa strength at 1250 °C, surpassing all reported as-cast Nb-Si alloys. Microstructural analysis shows that the superior properties originate from the dispersed distribution of nanoscale γ′-Nb 5 Si 3 phase and crack deflection at phase boundaries with 67.6% lattice mismatch. Our results demonstrate that combining machine learning techniques with mechanistic understanding can accelerate the discovery of high temperature materials.",
  "abstract_shown": true,
  "affiliations": [
    "National Key Laboratory for Precision Hot Processing of Metals, Harbin Institute of Technology, Harbin, PR China",
    "National Key Laboratory for Precision Hot Processing of Metals, Harbin Institute of Technology, Harbin, PR China. chendezhi383@163.com",
    "National Key Laboratory for Precision Hot Processing of Metals, Harbin Institute of Technology, Harbin, PR China. ruirunchen@hit.edu.cn",
    "AiMaterials Research LLC, Santa Fe, NM, USA. turablookman@gmail.com"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 10,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Chao Xu",
      "orcid": "0000-0002-5099-0060"
    },
    {
      "affs": [
        1
      ],
      "name": "Dezhi Chen",
      "orcid": "0009-0000-9246-4141"
    },
    {
      "affs": [
        0
      ],
      "name": "Xiaofu Zhang"
    },
    {
      "affs": [
        0
      ],
      "name": "Qi Ming Wang",
      "orcid": "0000-0002-4207-189X"
    },
    {
      "affs": [
        0
      ],
      "name": "Jingyue Yu"
    },
    {
      "affs": [
        0
      ],
      "name": "Shu Wang"
    },
    {
      "affs": [
        0
      ],
      "name": "Jingjie Guo"
    },
    {
      "affs": [
        0
      ],
      "name": "Hengzhi Fu"
    },
    {
      "affs": [
        2
      ],
      "name": "Ruirun Chen"
    },
    {
      "affs": [
        3
      ],
      "name": "Turab Lookman",
      "orcid": "0000-0001-8122-5671"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/2pj8wb9138qz8.bib",
  "cited_by": 20,
  "doi": "10.1038/s41467-026-75353-6",
  "fulltext_url": null,
  "id": "2pj8wb9138qz8",
  "issns": [
    "2041-1723"
  ],
  "landing_url": "https://doi.org/10.1038/s41467-026-75353-6",
  "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": "W7167814003",
  "pdf_url": null,
  "pmcid": "PMC13483041",
  "pmid": "42425984",
  "published": "2026-07-10",
  "publisher": "Nature Portfolio",
  "ris": "https://research.draconitic.com/paper/2pj8wb9138qz8.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:27:00Z",
      "native_id": "W7167814003",
      "source": "openalex",
      "url": "https://doi.org/10.1038/s41467-026-75353-6"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "openalex:2505|Machine Learning in Materials Science",
      "subfield": "materials-polymers"
    }
  ],
  "title": "High temperature Nb-Si alloys using data science: optimization of fracture toughness and high-temperature strength",
  "title_text": "High temperature Nb-Si alloys using data science: optimization of fracture toughness and high-temperature strength",
  "type": "peer-reviewed",
  "updated_at": "2026-11-09T13:21:22Z",
  "url": "https://research.draconitic.com/paper/2pj8wb9138qz8",
  "venue": "Nature Communications",
  "versions": []
}