February 1, 2024 Dataset Open Access

Martinetto, Vincent; Shah, Karan; Cangi, Attila; Pribram-Jones, Aurora

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  <identifier identifierType="DOI">10.14278/rodare.2720</identifier>
  <creators>
    <creator>
      <creatorName>Martinetto, Vincent</creatorName>
      <givenName>Vincent</givenName>
      <familyName>Martinetto</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0001-6026-7397</nameIdentifier>
      <affiliation>Department of Chemistry and Biochemistry, University of California Merced, 5200 North Lake Rd., Merced, California 95343, USA</affiliation>
    </creator>
    <creator>
      <creatorName>Shah, Karan</creatorName>
      <givenName>Karan</givenName>
      <familyName>Shah</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-5480-2880</nameIdentifier>
      <affiliation>Center for Advanced Systems Understanding, 02826 Görlitz, Germany/Helmholtz-Zentrum Dresden-Rossendorf, Bautzner Landstraße 400, 01328 Dresden</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>Center for Advanced Systems Understanding, 02826 Görlitz, Germany/Helmholtz-Zentrum Dresden-Rossendorf, Bautzner Landstraße 400, 01328 Dresden</affiliation>
    </creator>
    <creator>
      <creatorName>Pribram-Jones, Aurora</creatorName>
      <givenName>Aurora</givenName>
      <familyName>Pribram-Jones</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0003-0244-1814</nameIdentifier>
      <affiliation>Department of Chemistry and Biochemistry, University of California Merced, 5200 North Lake Rd., Merced, California 95343, USA</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Inverting the Kohn-Sham equations with physics-informed machine learning</title>
  </titles>
  <publisher>Rodare</publisher>
  <publicationYear>2024</publicationYear>
  <subjects>
    <subject>density functional theory</subject>
    <subject>machine learning</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2024-02-01</date>
  </dates>
  <resourceType resourceTypeGeneral="Dataset"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://rodare.hzdr.de/record/2720</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsSupplementTo">10.48550/arXiv.2312.15301</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsIdenticalTo">https://www.hzdr.de/publications/Publ-38725</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.14278/rodare.2719</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://rodare.hzdr.de/communities/casus</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://rodare.hzdr.de/communities/matter</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://rodare.hzdr.de/communities/rodare</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://rodare.hzdr.de/communities/zrt</relatedIdentifier>
  </relatedIdentifiers>
  <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;p&gt;This data repository contains the datasets used in the paper &amp;quot;Inverting the Kohn-Sham equations with physics-informed machine learning&amp;quot;.&amp;nbsp;&lt;/p&gt;

&lt;p&gt;It contains the data generation scripts, datasets for the systems used in the paper (Single Well - 1D atom, Double Well - 1D diatomic molecule) and output potentials&amp;nbsp;generated by the physics-informed machine learning models (physics-informed neural networks and Fourier neural operators).&lt;/p&gt;</description>
  </descriptions>
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