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@PHDTHESIS{Hilgers:994325,
      author       = {Hilgers, Robin Alexander},
      othercontributors = {Blügel, Stefan and Wuttig, Matthias and Assent, Ira},
      title        = {{P}rediction of magnetic materials for energy and
                      information : combining data-analytics and first-principles
                      theory},
      school       = {RWTH Aachen University},
      type         = {Dissertation},
      address      = {Aachen},
      publisher    = {RWTH Aachen University},
      reportid     = {RWTH-2024-09243},
      pages        = {1 Online-Ressource : Illustrationen},
      year         = {2024},
      note         = {Veröffentlicht auf dem Publikationsserver der RWTH Aachen
                      University; Dissertation, RWTH Aachen University, 2024},
      abstract     = {The essential role of magnetic materials in information
                      technology and the corresponding energy consumption of data
                      storage centers is crucially underestimated in modern
                      society. Saving energy resources is the societal challenge
                      of the 21st century. One of the leading scientific
                      objectives is finding ways to reduce energy consumption and
                      make resource usage more efficient. This thesis aims to shed
                      light on possible contributions of materials science
                      simulations towards a green IT transformation by providing
                      workflows and best-practice guidelines for high-throughput
                      materials screening tasks. An instance of such a screening
                      task is the search for magnetic materials for the next
                      generation of storage and data processing devices. However,
                      as the simulation process itself is time-consuming, this
                      thesis explores not only the material phase space but also
                      the application opportunities for data science and machine
                      learning (ML) in the material’s property prediction
                      process. As a prime example of a complex magnetic material
                      property, which is a limiting quantity when it comes to
                      methodological applicability, the critical temperature},
      cin          = {137510 / 130000},
      ddc          = {530},
      cid          = {$I:(DE-82)137510_20140620$ / $I:(DE-82)130000_20140620$},
      pnm          = {HDS LEE - Helmholtz School for Data Science in Life, Earth
                      and Energy (HDS LEE) (HDS-LEE-20190612) /
                      Doktorandenprogramm (PHD-PROGRAM-20170404)},
      pid          = {G:(DE-Juel1)HDS-LEE-20190612 /
                      G:(DE-HGF)PHD-PROGRAM-20170404},
      typ          = {PUB:(DE-HGF)11},
      doi          = {10.18154/RWTH-2024-09243},
      url          = {https://publications.rwth-aachen.de/record/994325},
}