February 1, 2024 Dataset Open Access

Martinetto, Vincent; Shah, Karan; Cangi, Attila; Pribram-Jones, Aurora

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{
  "url": "https://rodare.hzdr.de/record/2720", 
  "keywords": [
    "density functional theory", 
    "machine learning"
  ], 
  "sameAs": [
    "https://www.hzdr.de/publications/Publ-38725"
  ], 
  "creator": [
    {
      "@type": "Person", 
      "@id": "https://orcid.org/0000-0001-6026-7397", 
      "affiliation": "Department of Chemistry and Biochemistry, University of California Merced, 5200 North Lake Rd., Merced, California 95343, USA", 
      "name": "Martinetto, Vincent"
    }, 
    {
      "@type": "Person", 
      "@id": "https://orcid.org/0000-0002-5480-2880", 
      "affiliation": "Center for Advanced Systems Understanding, 02826 G\u00f6rlitz, Germany/Helmholtz-Zentrum Dresden-Rossendorf, Bautzner Landstra\u00dfe 400, 01328 Dresden", 
      "name": "Shah, Karan"
    }, 
    {
      "@type": "Person", 
      "@id": "https://orcid.org/0000-0001-9162-262X", 
      "affiliation": "Center for Advanced Systems Understanding, 02826 G\u00f6rlitz, Germany/Helmholtz-Zentrum Dresden-Rossendorf, Bautzner Landstra\u00dfe 400, 01328 Dresden", 
      "name": "Cangi, Attila"
    }, 
    {
      "@type": "Person", 
      "@id": "https://orcid.org/0000-0003-0244-1814", 
      "affiliation": "Department of Chemistry and Biochemistry, University of California Merced, 5200 North Lake Rd., Merced, California 95343, USA", 
      "name": "Pribram-Jones, Aurora"
    }
  ], 
  "distribution": [
    {
      "@type": "DataDownload", 
      "contentUrl": "https://rodare.hzdr.de/api/files/3603a26b-9e2c-4f6c-8132-71fb1194c54a/piml_ks_inversion.zip", 
      "fileFormat": "zip"
    }
  ], 
  "@context": "https://schema.org/", 
  "datePublished": "2024-02-01", 
  "name": "Inverting the Kohn-Sham equations with physics-informed machine learning", 
  "@id": "https://doi.org/10.14278/rodare.2720", 
  "description": "<p>This data repository contains the datasets used in the paper &quot;Inverting the Kohn-Sham equations with physics-informed machine learning&quot;.&nbsp;</p>\n\n<p>It contains the data generation scripts, datasets for the systems used in the paper (Single Well - 1D atom, Double Well - 1D diatomic molecule) and output potentials&nbsp;generated by the physics-informed machine learning models (physics-informed neural networks and Fourier neural operators).</p>", 
  "@type": "Dataset", 
  "license": "https://creativecommons.org/licenses/by/4.0/legalcode", 
  "identifier": "https://doi.org/10.14278/rodare.2720"
}