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    "Korea Institute for Advanced Study , , ,",
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  "author_total": 7,
  "authors": [
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      "name": "Sangmin Oh",
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      "name": "Hyungmin An",
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      "name": "Seungwu Han",
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      "name": "Youngho Kang",
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  "bibtex": "https://research.draconitic.com/paper/3w40wepbwfqdp.bib",
  "cited_by": 9,
  "doi": "10.1021/acs.jcim.6c01103",
  "fulltext_url": null,
  "id": "3w40wepbwfqdp",
  "issns": [
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  "landing_url": "https://arxiv.org/abs/2604.10887",
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  "pdf_url": "https://arxiv.org/pdf/2604.10887",
  "pmcid": null,
  "pmid": "42474417",
  "published": "2026-07-20",
  "publisher": "American Chemical Society",
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  "subjects": [
    {
      "field": "chemistry",
      "origin": "openalex:2505|Machine Learning in Materials Science",
      "subfield": "materials-polymers"
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  ],
  "title": "A Lightweight Universal Machine-Learning Interatomic Potential via Knowledge Distillation for Scalable Atomistic Simulations",
  "title_text": "A Lightweight Universal Machine-Learning Interatomic Potential via Knowledge Distillation for Scalable Atomistic Simulations",
  "type": "peer-reviewed",
  "updated_at": "2026-11-08T13:20:56Z",
  "url": "https://research.draconitic.com/paper/3w40wepbwfqdp",
  "venue": "Journal of Chemical Information and Modeling",
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