December 11, 2020 Dataset Restricted Access

Ellis, J. A.; Cangi, A.; Modine, N. A.; Stephens, J. A.; Thompson, A. P.; Rajamanickam, S.

DataCite XML Export

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  <identifier identifierType="DOI">10.14278/rodare.646</identifier>
  <creators>
    <creator>
      <creatorName>Ellis, J. A.</creatorName>
      <givenName>J. A.</givenName>
      <familyName>Ellis</familyName>
      <affiliation>Sandia National Laboratories</affiliation>
    </creator>
    <creator>
      <creatorName>Cangi, A.</creatorName>
      <givenName>A.</givenName>
      <familyName>Cangi</familyName>
      <affiliation>Helmholtz-Zentrum Dresden-Rossendorf</affiliation>
    </creator>
    <creator>
      <creatorName>Modine, N. A.</creatorName>
      <givenName>N. A.</givenName>
      <familyName>Modine</familyName>
      <affiliation>Sandia National Laboratories</affiliation>
    </creator>
    <creator>
      <creatorName>Stephens, J. A.</creatorName>
      <givenName>J. A.</givenName>
      <familyName>Stephens</familyName>
      <affiliation>Sandia National Laboratories</affiliation>
    </creator>
    <creator>
      <creatorName>Thompson, A. P.</creatorName>
      <givenName>A. P.</givenName>
      <familyName>Thompson</familyName>
      <affiliation>Sandia National Laboratories</affiliation>
    </creator>
    <creator>
      <creatorName>Rajamanickam, S.</creatorName>
      <givenName>S.</givenName>
      <familyName>Rajamanickam</familyName>
      <affiliation>Sandia National Laboratories</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Accelerating Finite-temperature Kohn-Sham Density Functional Theory with Deep Neural Networks</title>
  </titles>
  <publisher>Rodare</publisher>
  <publicationYear>2020</publicationYear>
  <subjects>
    <subject>machine learning</subject>
    <subject>neural networks</subject>
    <subject>materials science</subject>
    <subject>density functional theory</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2020-12-11</date>
  </dates>
  <resourceType resourceTypeGeneral="Dataset"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://rodare.hzdr.de/record/646</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsSupplementTo">https://arxiv.org/abs/2010.04905</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsIdenticalTo">https://www.hzdr.de/publications/Publ-31857</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsReferencedBy">https://www.hzdr.de/publications/Publ-31603</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.14278/rodare.645</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://rodare.hzdr.de/communities/hzdr</relatedIdentifier>
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    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://rodare.hzdr.de/communities/rodare</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="info:eu-repo/semantics/restrictedAccess">Restricted Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;Output from electronic structure code (Quantum Espresso) that serves as training data for the machine-learning workflow of the related scientific publication (https://arxiv.org/abs/2010.04905).&lt;/p&gt;</description>
    <description descriptionType="Other">This is only a limited set of the entire output data. The remainder of the data will be made available at a later point once approval from the collaborating research institution (Sandia National Laboratories) has been granted. The source code of the associated machine learning framework will also be published at that stage.</description>
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