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@PHDTHESIS{Hilgers:1032226,
      author       = {Hilgers, Robin},
      othercontributors = {Blügel, Stefan and Wortmann, Daniel and Assent, Ira and
                          Wuttig, Matthias},
      title        = {{P}rediction of {M}agnetic {M}aterials for {E}nergy and
                      {I}nformation {C}ombining {D}ata-{A}nalytics and
                      {F}irst-{P}rinciples {T}heory},
      volume       = {288},
      school       = {RWTH Aachen University},
      type         = {Dissertation},
      address      = {Jülich},
      publisher    = {Forschungszentrum Jülich GmbH Zentralbibliothek, Verlag},
      reportid     = {FZJ-2024-06074},
      isbn         = {978-3-95806-795-0},
      series       = {Reihe Schlüsseltechnologien / Key Technologies},
      pages        = {xv, 215},
      year         = {2024},
      note         = {First published with 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
                      𝑇𝑐 of existing magnetic simulation data of Heusler
                      alloys will be predicted using ML models. The capability and
                      limitations of these models will be analyzed and discussed.
                      It is shown that it is possible to extract physical
                      relations and knowledge from trained ML models without any
                      prior knowledge of the underlying physics and system
                      mechanics. Whether a Heusler compound has a 𝑇𝑐 high
                      enough to be relevant for an application in magnetic data
                      storage and processing devices could be predicted with over
                      90 $\%$ accuracy using lightweight ML model algorithms on
                      typical materials science data set sizes. Beyond that, the
                      phenomenon of near half-metallicity in Heusler compounds was
                      examined, including the successful ML-based prediction of
                      compounds displaying this property which were not known to
                      be nearly half-metallic before (L21 Co2HfIn, XA Mn2TaGe, and
                      L21 Co2ScSn). This particular study used existing
                      first-principles data of full and inverse Heusler
                      compound’s spin-polarized density of states, in order to
                      screen publicly available structural and magnetic ab initio
                      data for compounds exhibiting near half-metallic properties.
                      The relations learned by the underlying ML models are
                      discussed and compared to a known physical model. It was
                      determined that ML models have the capability to extend and
                      complement known physical models and relations when applied
                      to existing (and potentially imperfect) data. Finally,
                      large-scale high-throughput ultrathin film simulations of
                      3𝑑 transition metal layers on face-centered cubic noble
                      metal substrates were performed to understand the magnetic
                      properties of these magnetic multilayer films, which are
                      predicted to represent well-suited host platforms for room
                      temperature stable Skyrmions and hence are considered
                      candidate materials for spintronics-based storage and data
                      processing device applications. Tailored to high-throughput
                      ab initio workflows, a scalable method—that increased the
                      overall convergence rate from 64.8 $\%$ to 94.3 $\%$ and
                      exhibited the potential to save up to 17 $\%$ of the
                      computational time required, as well as to reduce the number
                      of needed ab initio relaxation steps to relax a multilayer
                      film system by up to 29 $\%$ in this systematic study, while
                      being flexible enough also to be applicable to future use
                      cases—using the integration of batch learning into
                      high-throughput workflows, was developed. The use,
                      restrictions, implementation, starting conditions, and
                      benefits of ML-based techniques and explainable artificial
                      intelligence are discussed in depth in this thesis.},
      cin          = {PGI-1 / IAS-8},
      cid          = {I:(DE-Juel1)PGI-1-20110106 / I:(DE-Juel1)IAS-8-20210421},
      pnm          = {632 - Materials – Quantum, Complex and Functional
                      Materials (POF4-632) / HDS LEE - Helmholtz School for Data
                      Science in Life, Earth and Energy (HDS LEE)
                      (HDS-LEE-20190612)},
      pid          = {G:(DE-HGF)POF4-632 / G:(DE-Juel1)HDS-LEE-20190612},
      typ          = {PUB:(DE-HGF)3 / PUB:(DE-HGF)11},
      urn          = {urn:nbn:de:0001-2501160959561.990566976222},
      doi          = {10.34734/FZJ-2024-06074},
      url          = {https://juser.fz-juelich.de/record/1032226},
}