September 30, 2022 Dataset Open Access

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

Citation Style Language JSON Export

{
  "DOI": "10.14278/rodare.1851", 
  "version": "1.0.0", 
  "type": "dataset", 
  "issued": {
    "date-parts": [
      [
        2022, 
        9, 
        30
      ]
    ]
  }, 
  "abstract": "<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>", 
  "author": [
    {
      "family": "Fiedler, Lenz"
    }, 
    {
      "family": "Schmerler, Steve"
    }, 
    {
      "family": "Modine, Normand"
    }, 
    {
      "family": "Vogel, Dayton J."
    }, 
    {
      "family": "Popoola, Gabriel A."
    }, 
    {
      "family": "Thompson, Aidan"
    }, 
    {
      "family": "Rajamanickam, Sivasankaran"
    }, 
    {
      "family": "Cangi, Attila"
    }
  ], 
  "publisher": "Rodare", 
  "title": "Scripts and Models for \"Predicting electronic structures at any length scale with machine learning\"", 
  "id": "1851"
}