Dataset Open Access

Benchmarking Universal Machine Learning Interatomic Potentials on Elemental Systems

Tahmasbi, Hossein; Knüpfer, Andreas; Kühne, Thomas Dae-Song; Mir Hosseini, Seyed Hossein

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{
  "issued": {
    "date-parts": [
      [
        2026, 
        4, 
        9
      ]
    ]
  }, 
  "type": "dataset", 
  "title": "Benchmarking Universal Machine Learning Interatomic Potentials on Elemental Systems", 
  "id": "4596", 
  "DOI": "10.14278/rodare.4596", 
  "publisher": "Rodare", 
  "abstract": "<p>Reference data and scripts generated for&nbsp;the &quot;Benchmarking Universal Machine Learning Interatomic<br>\nPotentials on Elemental Systems&quot; manuscript.</p>", 
  "author": [
    {
      "family": "Tahmasbi, Hossein"
    }, 
    {
      "family": "Kn\u00fcpfer, Andreas"
    }, 
    {
      "family": "K\u00fchne, Thomas Dae-Song"
    }, 
    {
      "family": "Mir Hosseini, Seyed Hossein"
    }
  ]
}
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Publication date:
April 9, 2026
DOI:
10.14278/rodare.4596

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10.14278/rodare.4596

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https://www.hzdr.de/publications/Publ-43234
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Creative Commons Attribution 4.0 International

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Version 1 10.14278/rodare.4596 Apr 9, 2026
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