September 30, 2022 Dataset Open Access

Fiedler, Lenz; Schmerler, Steve; Modine, Normand; Vogel, Dayton J.; Popoola, Gabriel A.; Thompson, Aidan; Rajamanickam, Sivasankaran; Cangi, Attila

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{
  "creator": [
    {
      "@id": "https://orcid.org/0000-0002-8311-0613", 
      "@type": "Person", 
      "name": "Fiedler, Lenz", 
      "affiliation": "HZDR / CASUS"
    }, 
    {
      "@id": "https://orcid.org/0000-0003-1354-0578", 
      "@type": "Person", 
      "name": "Schmerler, Steve", 
      "affiliation": "HZDR"
    }, 
    {
      "@type": "Person", 
      "name": "Modine, Normand", 
      "affiliation": "Sandia National Laboratories"
    }, 
    {
      "@id": "https://orcid.org/0000-0003-3612-0699", 
      "@type": "Person", 
      "name": "Vogel, Dayton J.", 
      "affiliation": "Sandia National Laboratories"
    }, 
    {
      "@type": "Person", 
      "name": "Popoola, Gabriel A.", 
      "affiliation": "Elder Research, Inc."
    }, 
    {
      "@id": "https://orcid.org/0000-0002-0324-9114", 
      "@type": "Person", 
      "name": "Thompson, Aidan", 
      "affiliation": "Sandia National Laboratories"
    }, 
    {
      "@id": "https://orcid.org/0000-0002-5854-409X", 
      "@type": "Person", 
      "name": "Rajamanickam, Sivasankaran", 
      "affiliation": "Sandia National Laboratories"
    }, 
    {
      "@id": "https://orcid.org/0000-0001-9162-262X", 
      "@type": "Person", 
      "name": "Cangi, Attila", 
      "affiliation": "HZDR / CASUS"
    }
  ], 
  "distribution": [
    {
      "@type": "DataDownload", 
      "fileFormat": "zip", 
      "contentUrl": "https://rodare.hzdr.de/api/files/a7d766b9-2422-4f12-8062-91d64d6f6b97/size_transfer_cleaned.zip"
    }
  ], 
  "datePublished": "2022-09-30", 
  "version": "1.0.0", 
  "@id": "https://doi.org/10.14278/rodare.1851", 
  "identifier": "https://doi.org/10.14278/rodare.1851", 
  "license": "https://creativecommons.org/licenses/by/4.0/legalcode", 
  "@context": "https://schema.org/", 
  "description": "<pre><strong>Scripts and Models for &quot;Predicting the Electronic Structure of Matter on Ultra-Large Scales&quot;</strong>\n\nThis data set contains scripts and models to reproduce the results of our manuscript &quot;Physics-informed Machine Learning \nModels for Scalable Density Functional Theory Calculations&quot;. The scripts are supposed to be used in conjunction\nwith the ab-initio data sets also published alongside our research article. \n\n<em>Requirements</em>\n<em>\n</em>python&gt;=3.7.x\nmala&gt;=1.1.0\nase\nnumpy\n\n<em>Contents</em>\n\n| Folder name      | Description                                      |\n|------------------|--------------------------------------------------|\n| data_analysis/   | Run script for RDF calculations        |\n| model_inference/ | Run script to run inference based on MALA models |\n| model_training/  | Run script to train MALA models                  |\n| trained_models/  | Trained models for beryllium and aluminium       |\n</pre>", 
  "name": "Scripts and Models for \"Predicting electronic structures at any length scale with machine learning\"", 
  "sameAs": [
    "https://www.hzdr.de/publications/Publ-35305"
  ], 
  "url": "https://rodare.hzdr.de/record/1851", 
  "@type": "Dataset"
}