May 14, 2025 Dataset Open Access

Fiedler, Lenz; Cangi, Attila

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  <identifier identifierType="DOI">10.14278/rodare.3746</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</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</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Boron data set for machine learning applications</title>
  </titles>
  <publisher>Rodare</publisher>
  <publicationYear>2025</publicationYear>
  <dates>
    <date dateType="Issued">2025-05-14</date>
  </dates>
  <resourceType resourceTypeGeneral="Dataset"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://rodare.hzdr.de/record/3746</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsIdenticalTo">https://www.hzdr.de/publications/Publ-41336</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsReferencedBy">https://www.hzdr.de/publications/Publ-40059</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.14278/rodare.3745</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;p&gt;&lt;strong&gt;Boron data set for machine learning applications&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;This dataset contains DFT inputs, outputs, LDOS data and bispectrum descriptor vectors for an &amp;alpha;-rhombohedral boron cell of 144 atoms at room temperature and ambient mass density. All simulations have been performed at an LDOS converged k-grid of 4x4x4 k-points.&lt;/p&gt;

&lt;p&gt;This dataset contains one .zip file for each of its five type of data (bispectrum descriptors, LDOS, DFT inputs, DFT outputs and trained models).&lt;/p&gt;

&lt;p&gt;&lt;em&gt;Authors:&lt;/em&gt;&lt;/p&gt;

&lt;p&gt;- Fiedler, Lenz (HZDR / CASUS)&lt;br&gt;
- Cangi, Attila (HZDR / CASUS)&lt;/p&gt;

&lt;p&gt;Affiliations&lt;em&gt;:&lt;/em&gt;&lt;/p&gt;

&lt;p&gt;HZDR - Helmholtz-Zentrum Dresden-Rossendorf&lt;br&gt;
CASUS - Center for Advanced Systems Understanding&lt;/p&gt;

&lt;p&gt;&lt;em&gt;Dataset description&lt;/em&gt;&lt;/p&gt;

&lt;p&gt;- Total size: 26 GB&lt;br&gt;
- System: B144&lt;br&gt;
- Temperature(s): 298K&lt;br&gt;
- Mass density(ies): 2.483 gcc&lt;br&gt;
- Crystal Structure: amorphous (material mp-160 in the materials project)&lt;br&gt;
- Number of atomic snapshots: 15&lt;br&gt;
- Contents:&lt;br&gt;
&amp;nbsp;&amp;nbsp;&amp;nbsp; - ideal crystal structure: no&lt;br&gt;
&amp;nbsp;&amp;nbsp;&amp;nbsp; - MD trajectory: no&lt;br&gt;
&amp;nbsp;&amp;nbsp;&amp;nbsp; - Atomic positions: no&lt;br&gt;
&amp;nbsp;&amp;nbsp;&amp;nbsp; - DFT inputs: yes&lt;br&gt;
&amp;nbsp;&amp;nbsp;&amp;nbsp; - DFT outputs (energies): yes&lt;br&gt;
&amp;nbsp;&amp;nbsp;&amp;nbsp; - SNAP vectors: yes&lt;br&gt;
&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp; - dimensions: 108x108x35x94 (last dimension: first three entries are x,y,z coordinates, data size is 91)&lt;br&gt;
&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp; - units: a.u.&lt;br&gt;
&amp;nbsp;&amp;nbsp;&amp;nbsp; - LDOS vectors: yes&lt;br&gt;
&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp; - dimensions: 108x108x35x241&lt;br&gt;
&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp; - units: 1/(eV*Angstrom^3)&lt;br&gt;
&amp;nbsp;&amp;nbsp;&amp;nbsp; - trained networks: yes&lt;/p&gt;

&lt;p&gt;&lt;br&gt;
&lt;em&gt;Dataset structure&lt;/em&gt;&lt;/p&gt;

&lt;p&gt;A .zip file is included for each for each of its five type of data:&lt;/p&gt;

&lt;p&gt;- ldos.zip: holds the LDOS vectors (one HDF5 file per snapshot)&lt;br&gt;
- bispectrum.zip: holds the bispectrum fingerprint vectors&amp;nbsp; (one HDF5 file per snapshot)&lt;br&gt;
- dft_outputs: holds the outputs from the DFT calculations, i.e. energies and simulation parameters in a .json format (one per snapshot)&lt;br&gt;
- dft_inputs: holds the inputs for the DFT calculations, in the form of a QE input file (one per snapshot)&lt;br&gt;
- models: holds five trained NN models for the data set&lt;/p&gt;</description>
  </descriptions>
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