Dataset of self-consistent Hubbard parameters for Ni, Mn and Fe from linear-response

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  <dc:creator>Uhrin, Martin</dc:creator>
  <dc:creator>Zadoks, Austin</dc:creator>
  <dc:creator>Binci, Luca</dc:creator>
  <dc:creator>Marzari, Nicola</dc:creator>
  <dc:creator>Timrov, Iurii</dc:creator>
  <dc:date>2024-10-15</dc:date>
  <dc:description>Density-functional theory with extended Hubbard functionals (DFT+U+V) provides a robust framework to accurately describe complex materials containing transition-metal or rare-earth elements. It does so by mitigating self-interaction errors inherent to semi-local functionals which are particularly pronounced in systems with partially-filled d and f electronic states. However, achieving accuracy in this approach hinges upon the accurate determination of the on-site U and inter-site V Hubbard parameters. In practice, these are obtained either by semi-empirical tuning, requiring prior knowledge, or, more correctly, by using predictive but expensive first-principles calculations.  This archive entry contains Hubbard parameters, occupation matrices and other data calculated for 28 materials and covers all steps in a self-consistent procedure where, at each step new Hubbard parameters are obtained via linear-response, a process that is repeated until the parameters no longer change. The primary purpose of this dataset is to support the development and validation of machine learning models that can be used to predict Hubbard parameters, sidestepping the need for expensive ab-initio density functional perturbation theory calculations.</dc:description>
  <dc:identifier>https://materialscloud-archive-failover.cineca.it/record/2024.160</dc:identifier>
  <dc:identifier>doi:10.24435/materialscloud:r5-42</dc:identifier>
  <dc:identifier>mcid:2024.160</dc:identifier>
  <dc:identifier>oai:materialscloud.org:2389</dc:identifier>
  <dc:language>en</dc:language>
  <dc:publisher>Materials Cloud</dc:publisher>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>Creative Commons Attribution Share Alike 4.0 International https://creativecommons.org/licenses/by-sa/4.0/legalcode</dc:rights>
  <dc:subject>machine learning</dc:subject>
  <dc:subject>Hubbard corrections</dc:subject>
  <dc:subject>density-functional theory</dc:subject>
  <dc:subject>MARVEL</dc:subject>
  <dc:title>Dataset of self-consistent Hubbard parameters for Ni, Mn and Fe from linear-response</dc:title>
  <dc:type>Dataset</dc:type>
</oai_dc:dc>