December 11, 2020 Dataset Restricted Access

Ellis, J. A.; Cangi, A.; Modine, N. A.; Stephens, J. A.; Thompson, A. P.; Rajamanickam, S.

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{
  "description": "<p>Output from electronic structure code (Quantum Espresso) that serves as training data for the machine-learning workflow of the related scientific publication (https://arxiv.org/abs/2010.04905).</p>", 
  "datePublished": "2020-12-11", 
  "@id": "https://doi.org/10.14278/rodare.646", 
  "name": "Accelerating Finite-temperature Kohn-Sham Density Functional Theory with Deep Neural Networks", 
  "identifier": "https://doi.org/10.14278/rodare.646", 
  "@type": "Dataset", 
  "creator": [
    {
      "affiliation": "Sandia National Laboratories", 
      "@type": "Person", 
      "name": "Ellis, J. A."
    }, 
    {
      "affiliation": "Helmholtz-Zentrum Dresden-Rossendorf", 
      "@type": "Person", 
      "name": "Cangi, A."
    }, 
    {
      "affiliation": "Sandia National Laboratories", 
      "@type": "Person", 
      "name": "Modine, N. A."
    }, 
    {
      "affiliation": "Sandia National Laboratories", 
      "@type": "Person", 
      "name": "Stephens, J. A."
    }, 
    {
      "affiliation": "Sandia National Laboratories", 
      "@type": "Person", 
      "name": "Thompson, A. P."
    }, 
    {
      "affiliation": "Sandia National Laboratories", 
      "@type": "Person", 
      "name": "Rajamanickam, S."
    }
  ], 
  "@context": "https://schema.org/", 
  "url": "https://rodare.hzdr.de/record/646", 
  "sameAs": [
    "https://www.hzdr.de/publications/Publ-31857"
  ], 
  "keywords": [
    "machine learning", 
    "neural networks", 
    "materials science", 
    "density functional theory"
  ]
}