{
  "abstract": "opportunities, risks and engineering practices for LLM-based data",
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  "affiliations": [
    "Beijing Key Laboratory of Intelligent Design and Manufacturing for Hydrogen Energy Materials , Beijing University of Chemical Technology , Beijing 100029 , China",
    "Deep Intelligence Experiment Technology ( Beijing) Co., Ltd. , Beijing 100029 , China",
    "Correspondence to: Prof. Haoxiang Xu, Prof. Daojian Cheng, Beijing Key Laboratory of Intelligent Design and Manufacturing for Hydrogen Energy Materials, Beijing University of Chemical Technology, Beijing 100029, China. E-mail: xuhx@mail.buct.edu.cn; chengdj@mail.buct.edu.cn"
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      "name": "Weijian Chen"
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      "name": "Haoxiang Xu"
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  "doi": "10.20517/aiagent.2026.06",
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  "pdf_url": "https://f.oaes.cc/xmlpdf/published/article/b278ddbae77bb346d164dd2316a4fd15/aiagent2006.pdf",
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  "published": "2026-06-03",
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  "ris": "https://research.draconitic.com/paper/94vv4caaxasps.ris",
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  "subjects": [
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      "origin": "openalex:2505|Machine Learning in Materials Science",
      "subfield": "materials-polymers"
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  "title": "From unstructured to structured: opportunities, risks and engineering practices for LLM-based data extraction in chemistry and materials",
  "title_text": "From unstructured to structured: opportunities, risks and engineering practices for LLM-based data extraction in chemistry and materials",
  "type": "peer-reviewed",
  "updated_at": "2026-11-06T13:20:45Z",
  "url": "https://research.draconitic.com/paper/94vv4caaxasps",
  "venue": "AI Agent",
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