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    <title>Researchdata.se</title>
    <description>Search results</description>
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    <item>
      <title>Models and data supporting the paper "Predicting neutron experiments from first principles: A workflow powered by machine learning"</title>
      <description>This record accompanies the publication "Predicting neutron experiments from first principles: A workflow powered by machine learning". It comprises the machine-learned interatomic potentials (MLIPs) constructed and employed in that work with their respective training data as well as the experimental inelastic neutron scattering data for crystalline benzene presented in the publication.

Hydrogenated Sc-doped BaTiO3

nep-BaScTiOH.txt – MLIP based on the neuroevolution potential (NEP) form

nep-BaScTiOH.zip – model ensemble with the underlying training and validation data

BaScTiOH-R2SCAN.db – database with reference data, in sql-lite format, readable using the ase package

Benzene

nep-benzene.txt – MLIP based on the neuroevolution potential (NEP) form

nep-benzene.zip – model ensemble with the underlying training and validation data

benzene-CX.db – database with reference data, in sql-lite format, readable using the ase package

reduced-benzene-tosca.zip – experimental inelastic neutron scattering data</description>
      <pubDate>Fri, 04 Jul 2025 00:00:00 GMT</pubDate>
      <link>https://researchdata.se/en/catalogue/dataset/doi-10-5281-zenodo-15809229</link>
      <guid>https://researchdata.se/en/catalogue/dataset/doi-10-5281-zenodo-15809229</guid>
      <dc:publisher>Chalmers University of Technology</dc:publisher>
      <dc:creator>Lindgren, Eric</dc:creator>
      <dc:creator>Jackson, Adam J.</dc:creator>
      <dc:creator>Fransson, Erik</dc:creator>
      <dc:creator>Berger, Esmée</dc:creator>
      <dc:creator>Rudic, Svemir</dc:creator>
      <dc:creator>Skoro, Goran</dc:creator>
      <dc:creator>Turanyi, Rastislav</dc:creator>
      <dc:creator>Mukhopadhyay, Sanghamitra</dc:creator>
      <dc:creator>Erhart, Paul</dc:creator>
    </item>
    <item>
      <title>Data and scripts for "Optical line shapes of color centers in solids from classical autocorrelation functions"</title>
      <description>This record contains data and code that accompany the paper "Optical line shapes of color centers in solids from classical autocorrelation functions". Specifically it includea databases in ase sqlite format (*.db) with reference data from density functional theory calculations. These data were used in the construction of the machine-learned potential model using the neuroevolution potential (NEP) methodology. The model (nep.txt) is included in a format suitable for the GPUMD package (https://gpumd.org).

Note that the atom type information in the databases already includes the labeling of the defect environment that is expected by the NEP model, according to the following rules

Si(gs) →  P

C(gs) → N

Si(ex) → S

C(ex) → O

The "bulk" species (Si, C) remain unchanged.</description>
      <pubDate>Fri, 09 Aug 2024 00:00:00 GMT</pubDate>
      <link>https://researchdata.se/en/catalogue/dataset/doi-10-5281-zenodo-13284739</link>
      <guid>https://researchdata.se/en/catalogue/dataset/doi-10-5281-zenodo-13284739</guid>
      <dc:publisher>Chalmers University of Technology</dc:publisher>
      <dc:creator>Linderälv, Christopher</dc:creator>
      <dc:creator>Österbacka, Nicklas</dc:creator>
      <dc:creator>Wiktor, Julia</dc:creator>
      <dc:creator>Erhart, Paul</dc:creator>
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