{
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  "affiliations": [
    "Department of Civil and Environmental Engineering, Indian Institute of Technology Delhi, Hauz Khas, India",
    "Yardi School of Artificial Intelligence, Indian Institute of Technology Delhi, Hauz Khas, India",
    "Intel Labs, Santa Clara, CA, USA",
    "NVIDIA Corporation, Santa Clara, CA, USA",
    "Department of Materials Science and Engineering, Indian Institute of Technology Delhi, Hauz Khas, India",
    "Department of Computer Science and Engineering, Indian Institute of Technology Delhi, Hauz Khas, India",
    "Intel Labs, Santa Clara, CA, USA. santiago.miret@intel.com",
    "Department of Civil and Environmental Engineering, Indian Institute of Technology Delhi, Hauz Khas, India. krishnan@iitd.ac.in",
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      "name": "N M Anoop Krishnan",
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  "bibtex": "https://research.draconitic.com/paper/17pv5g5w1dm1y.bib",
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  "doi": "10.1038/s43588-026-01019-4",
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  "landing_url": "https://arxiv.org/abs/2508.05762",
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  "pdf_url": "https://arxiv.org/pdf/2508.05762",
  "pmcid": null,
  "pmid": "42448888",
  "published": "2026-07-14",
  "publisher": "Nature Portfolio",
  "ris": "https://research.draconitic.com/paper/17pv5g5w1dm1y.ris",
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  "subjects": [
    {
      "field": "chemistry",
      "origin": "openalex:2505|Machine Learning in Materials Science",
      "subfield": "materials-polymers"
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  "title": "UniFFBench: evaluating universal machine learning force fields against experimental measurements",
  "title_text": "UniFFBench: evaluating universal machine learning force fields against experimental measurements",
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  "updated_at": "2026-11-09T13:21:22Z",
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  "venue": "Nature Computational Science",
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