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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    "title": "Scripts and Models for \"Predicting electronic structures at any length scale with machine learning\"", 
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        "name": "Schmerler, Steve", 
        "affiliation": "HZDR", 
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        "name": "Modine, Normand", 
        "affiliation": "Sandia National Laboratories"
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        "affiliation": "Sandia National Laboratories", 
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        "affiliation": "Elder Research, Inc."
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        "affiliation": "Sandia National Laboratories", 
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        "affiliation": "Sandia National Laboratories", 
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    "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>", 
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