March 25, 2024 Dataset Open Access

Tahmasbi, Hossein; Ramakrishna, Kushal; Lokamani, Mani; Cangi, Attila

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  <identifier identifierType="DOI">10.14278/rodare.2778</identifier>
  <creators>
    <creator>
      <creatorName>Tahmasbi, Hossein</creatorName>
      <givenName>Hossein</givenName>
      <familyName>Tahmasbi</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-3072-8217</nameIdentifier>
    </creator>
    <creator>
      <creatorName>Ramakrishna, Kushal</creatorName>
      <givenName>Kushal</givenName>
      <familyName>Ramakrishna</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0003-4211-2484</nameIdentifier>
    </creator>
    <creator>
      <creatorName>Lokamani, Mani</creatorName>
      <givenName>Mani</givenName>
      <familyName>Lokamani</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0001-8679-5905</nameIdentifier>
    </creator>
    <creator>
      <creatorName>Cangi, Attila</creatorName>
      <givenName>Attila</givenName>
      <familyName>Cangi</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0001-9162-262X</nameIdentifier>
    </creator>
  </creators>
  <titles>
    <title>Data publication: Machine Learning-Driven Structure Prediction for Iron Hydrides</title>
  </titles>
  <publisher>Rodare</publisher>
  <publicationYear>2024</publicationYear>
  <dates>
    <date dateType="Issued">2024-03-25</date>
  </dates>
  <resourceType resourceTypeGeneral="Dataset"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://rodare.hzdr.de/record/2778</alternateIdentifier>
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  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsIdenticalTo">https://www.hzdr.de/publications/Publ-38894</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsReferencedBy">https://www.hzdr.de/publications/Publ-37800</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.14278/rodare.2777</relatedIdentifier>
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  <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>
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  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;Here, we provide the training datasets and the resulting neural network potential for exploring the potential energy surfaces of the FeH system using the minima hopping method. Additionally, data for the minima structures identified in this work are included.&lt;/p&gt;</description>
  </descriptions>
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