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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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  <identifier identifierType="DOI">10.14278/rodare.1851</identifier>
  <creators>
    <creator>
      <creatorName>Fiedler, Lenz</creatorName>
      <givenName>Lenz</givenName>
      <familyName>Fiedler</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-8311-0613</nameIdentifier>
      <affiliation>HZDR / CASUS</affiliation>
    </creator>
    <creator>
      <creatorName>Schmerler, Steve</creatorName>
      <givenName>Steve</givenName>
      <familyName>Schmerler</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0003-1354-0578</nameIdentifier>
      <affiliation>HZDR</affiliation>
    </creator>
    <creator>
      <creatorName>Modine, Normand</creatorName>
      <givenName>Normand</givenName>
      <familyName>Modine</familyName>
      <affiliation>Sandia National Laboratories</affiliation>
    </creator>
    <creator>
      <creatorName>Vogel, Dayton J.</creatorName>
      <givenName>Dayton J.</givenName>
      <familyName>Vogel</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0003-3612-0699</nameIdentifier>
      <affiliation>Sandia National Laboratories</affiliation>
    </creator>
    <creator>
      <creatorName>Popoola, Gabriel A.</creatorName>
      <givenName>Gabriel A.</givenName>
      <familyName>Popoola</familyName>
      <affiliation>Elder Research, Inc.</affiliation>
    </creator>
    <creator>
      <creatorName>Thompson, Aidan</creatorName>
      <givenName>Aidan</givenName>
      <familyName>Thompson</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-0324-9114</nameIdentifier>
      <affiliation>Sandia National Laboratories</affiliation>
    </creator>
    <creator>
      <creatorName>Rajamanickam, Sivasankaran</creatorName>
      <givenName>Sivasankaran</givenName>
      <familyName>Rajamanickam</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-5854-409X</nameIdentifier>
      <affiliation>Sandia National Laboratories</affiliation>
    </creator>
    <creator>
      <creatorName>Cangi, Attila</creatorName>
      <givenName>Attila</givenName>
      <familyName>Cangi</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0001-9162-262X</nameIdentifier>
      <affiliation>HZDR / CASUS</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Scripts and Models for "Predicting electronic structures at any length scale with machine learning"</title>
  </titles>
  <publisher>Rodare</publisher>
  <publicationYear>2022</publicationYear>
  <dates>
    <date dateType="Issued">2022-09-30</date>
  </dates>
  <resourceType resourceTypeGeneral="Dataset"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://rodare.hzdr.de/record/1851</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsIdenticalTo">https://www.hzdr.de/publications/Publ-35305</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsReferencedBy">https://www.hzdr.de/publications/Publ-35418</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.14278/rodare.1850</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://rodare.hzdr.de/communities/rodare</relatedIdentifier>
  </relatedIdentifiers>
  <version>1.0.0</version>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;pre&gt;&lt;strong&gt;Scripts and Models for &amp;quot;Predicting the Electronic Structure of Matter on Ultra-Large Scales&amp;quot;&lt;/strong&gt;

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

&lt;em&gt;Requirements&lt;/em&gt;
&lt;em&gt;
&lt;/em&gt;python&amp;gt;=3.7.x
mala&amp;gt;=1.1.0
ase
numpy

&lt;em&gt;Contents&lt;/em&gt;

| 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       |
&lt;/pre&gt;</description>
  </descriptions>
</resource>
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