April 20, 2023 Dataset Open Access

Fiedler, Lenz; Modine, Normand A.; Miller, Kyle D.; Cangi, Attila

DataCite XML Export

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  <identifier identifierType="DOI">10.14278/rodare.2266</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>Modine, Normand A.</creatorName>
      <givenName>Normand A.</givenName>
      <familyName>Modine</familyName>
      <affiliation>Sandia National Laboratories</affiliation>
    </creator>
    <creator>
      <creatorName>Miller, Kyle D.</creatorName>
      <givenName>Kyle D.</givenName>
      <familyName>Miller</familyName>
      <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 "Machine learning the electronic structure of matter across temperatures"</title>
  </titles>
  <publisher>Rodare</publisher>
  <publicationYear>2023</publicationYear>
  <dates>
    <date dateType="Issued">2023-04-20</date>
  </dates>
  <resourceType resourceTypeGeneral="Dataset"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://rodare.hzdr.de/record/2266</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsIdenticalTo">https://www.hzdr.de/publications/Publ-36845</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsReferencedBy">https://www.hzdr.de/publications/Publ-39797</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsReferencedBy">https://www.hzdr.de/publications/Publ-37111</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.14278/rodare.2265</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://rodare.hzdr.de/communities/rodare</relatedIdentifier>
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  <version>v1.0.0</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;em&gt;# &lt;/em&gt;Data and Scripts for &amp;quot;Machine learning the electronic structure of matter across temperatures&amp;quot;

This dataset contains data and calculation scripts for the publication &amp;quot;Machine learning the electronic structure of matter across temperatures&amp;quot;.
Its goal is to enable interested parties to reproduce the experiments we have carried out.

&lt;em&gt;## &lt;/em&gt;Prerequesites

The following software versions are needed for the python scripts:

&lt;em&gt;- &lt;/em&gt;`python`: 3.8.x
&lt;em&gt;- &lt;/em&gt;`mala`: 1.2.0 
&lt;em&gt;- &lt;/em&gt;`numpy`: 1.23.0 (lower version may work)

Further, make sure you have downloaded additional data such as local pseudopotentials and training data.

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

&lt;em&gt;- &lt;/em&gt;`data_analysis/`: Contains scripts contain useful functions to reproduce the analysis carried out on the provided 
                    data.
&lt;em&gt;- &lt;/em&gt;`model_training/`: Contains scripts that allow the training and testing of the models discussed in the accompanying
                     publication.
&lt;em&gt;- &lt;/em&gt;`trained_models`: Contains the models discussed in the accompanying publication. Per data set, five models with 
                    different random initializations were trained. 
&lt;/pre&gt;</description>
  </descriptions>
</resource>