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@PHDTHESIS{Redies:909289,
      author       = {Redies, Matthias},
      title        = {{H}igh-{P}erformance {C}omputing {A}pproach to {H}ybrid
                      {F}unctionals in the {A}ll-{E}lectron {DFT} {C}ode {FLEUR}},
      volume       = {257},
      school       = {RWTH Aachen University},
      type         = {Dissertation},
      address      = {Jülich},
      publisher    = {Forschungszentrum Jülich GmbH Zentralbibliothek, Verlag},
      reportid     = {FZJ-2022-03102},
      isbn         = {978-3-95806-639-7},
      series       = {Schriften des Forschungszentrums Jülich Reihe
                      Schlüsseltechnologien / Key Technologies},
      pages        = {xi, 109},
      year         = {2022},
      note         = {Dissertation, RWTH Aachen University, 2022},
      abstract     = {Virtual materials design attempts to use computational
                      methods to discover new materials with superior properties
                      within the vast space of all conceivable materials.
                      Density-functional theory (DFT) is central to this field,
                      enabling scientists to predict material properties from
                      first principles, i.e. without relying on external
                      parameters or experimental values. While standard DFT is
                      capable of predicting many materials with satisfying
                      accuracy, it struggles with some properties such as details
                      of the electronic structure or certain material classes,
                      e.g. materials exhibiting strongly correlated electrons.
                      This has created a need for methods with greater predictive
                      power. One such class of methods are hybrid
                      exchange-correlation functionals which combine the exact
                      Hartree-Fock exchange with local exchange-correlation
                      functionals, resulting in highly accurate predictions for
                      many insulating or semiconductor materials. However, the
                      computational cost of hybrid functionals increases rapidly
                      with system size and limits their application to small
                      systems. This thesis aims to solve the computational
                      challenge posed by hybrid functionals in large systems by
                      utilizing the massive computational power of today’s
                      supercomputers},
      cin          = {PGI-1 / IAS-1 / JARA-FIT / JARA-HPC},
      cid          = {I:(DE-Juel1)PGI-1-20110106 / I:(DE-Juel1)IAS-1-20090406 /
                      $I:(DE-82)080009_20140620$ / $I:(DE-82)080012_20140620$},
      pnm          = {5211 - Topological Matter (POF4-521)},
      pid          = {G:(DE-HGF)POF4-5211},
      typ          = {PUB:(DE-HGF)3 / PUB:(DE-HGF)11},
      url          = {https://juser.fz-juelich.de/record/909289},
}