Dataset Open Access

Scripts and Models for "Predicting electronic structures at any length scale with machine learning"

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


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  <dc:creator>Fiedler, Lenz</dc:creator>
  <dc:creator>Schmerler, Steve</dc:creator>
  <dc:creator>Modine, Normand</dc:creator>
  <dc:creator>Vogel, Dayton J.</dc:creator>
  <dc:creator>Popoola, Gabriel A.</dc:creator>
  <dc:creator>Thompson, Aidan</dc:creator>
  <dc:creator>Rajamanickam, Sivasankaran</dc:creator>
  <dc:creator>Cangi, Attila</dc:creator>
  <dc:date>2022-09-30</dc:date>
  <dc:description>Scripts and Models for "Predicting the Electronic Structure of Matter on Ultra-Large Scales"

This data set contains scripts and models to reproduce the results of our manuscript "Physics-informed Machine Learning 
Models for Scalable Density Functional Theory Calculations". The scripts are supposed to be used in conjunction
with the ab-initio data sets also published alongside our research article. 

Requirements

python&gt;=3.7.x
mala&gt;=1.1.0
ase
numpy

Contents

| Folder name      | Description                                      |
|------------------|--------------------------------------------------|
| data_analysis/   | Run script for RDF calculations        |
| model_inference/ | Run script to run inference based on MALA models |
| model_training/  | Run script to train MALA models                  |
| trained_models/  | Trained models for beryllium and aluminium       |
</dc:description>
  <dc:identifier>https://rodare.hzdr.de/record/1851</dc:identifier>
  <dc:identifier>10.14278/rodare.1851</dc:identifier>
  <dc:identifier>oai:rodare.hzdr.de:1851</dc:identifier>
  <dc:relation>url:https://www.hzdr.de/publications/Publ-35305</dc:relation>
  <dc:relation>url:https://www.hzdr.de/publications/Publ-35418</dc:relation>
  <dc:relation>doi:10.14278/rodare.1850</dc:relation>
  <dc:relation>url:https://rodare.hzdr.de/communities/rodare</dc:relation>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>https://creativecommons.org/licenses/by/4.0/legalcode</dc:rights>
  <dc:title>Scripts and Models for "Predicting electronic structures at any length scale with machine learning"</dc:title>
  <dc:type>info:eu-repo/semantics/other</dc:type>
  <dc:type>dataset</dc:type>
</oai_dc:dc>
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