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@INPROCEEDINGS{Hilgers:1016528,
      author       = {Hilgers, Robin and Wortmann, Daniel and Blügel, Stefan},
      title        = {{M}agnetic {M}ultilayers: {F}rom {H}igh-{T}hroughput
                      {A}b-initio {C}alculations to {P}redictive {M}achine
                      {L}earning},
      reportid     = {FZJ-2023-03694},
      year         = {2023},
      abstract     = {Thin-film multi-layer systems of 3d transition metals
                      exhibiting magnetic phenomena are prototype systems in the
                      field of surface magnetism [1,2]. The possibility to tune
                      the interactions by choosing different elements and
                      different stacking, enables scientists and engineers to
                      design materials with specific desired magnetic properties.
                      Up to now, the magnetic properties are rarely examined using
                      high-throughput simulations due to the peculiarities of the
                      setup and technical challenges induced by the surface setup
                      in DFT [3] calculations.We performed a combinatorics study
                      of such multilayer surface systems by employing a layer
                      swapping based approach using three different mono-atomic 3d
                      transition metal layers on noble metal (FCC) substrates.
                      Hence, we systematically constructed symmetric thin-films
                      and computed the resulting magnetic properties. By using a
                      highly automated AiiDA [4,5] workflow and our all-electron
                      full-potential DFT code FLEUR [3] we studied 6660 possible
                      configurations of film systems. This systematic approach
                      enables us to perform a detailed analysis of the underlying
                      physics, magnetic properties as well as to apply ML and XAI
                      techniques on the acquired data. Concluding, we demonstrate
                      the capabilities of state-of-the-art computational
                      frameworks [4,5,6] and workflows [7] in high-throughput
                      materials screening.Acknowledgement: This work was performed
                      as part of the Helmholtz School for Data Science in Life,
                      Earth and Energy (HDS-LEE) and received funding from the
                      Helmholtz Association of German Research Centres.
                      References:[1] Zhang, L. (2022). Topological magnonic
                      properties of two-dimensional magnetic materials (Vol. 253,
                      p. 154 p.) [Dissertation, Forschungszentrum Jülich GmbH
                      Zentralbibliothek, Verlag].
                      https://juser.fz-juelich.de/record/907375[2] Blügel S.
                      Two-dimensional ferromagnetism of 3d, 4d, and 5d transition
                      metal monolayers on noble metal (001) substrates. Phys Rev
                      Lett. 1992 Feb 10;68(6):851-854. doi:
                      10.1103/PhysRevLett.68.851. PMID: 10046009.[3] FLEUR Code
                      www.flapw.de[4] S. P. Huber et al., AiiDA 1.0, a scalable
                      computational infrastructure for automated reproducible
                      workflows and data provenance, Scientific Data 7, 300
                      (2020); DOI: 10.1038/s41597-020-00638-4[5] Jens Bröder,
                      Vasily Tseplyaev, Henning Janssen, Anoop Chandran, Daniel
                      Wortmann, $\&$ Stefan Blügel. (2022).
                      JuDFTteam/aiida-fleur: AiiDA-FLEUR (v.1.3.1). Zenodo.
                      https://doi.org/10.5281/zenodo.6420726[6] Bröder, J.
                      (2021). High-throughput All-Electron Density Functional
                      Theory Simulations for a Data-driven Chemical Interpretation
                      of X-ray Photoelectron Spectra (Vol. 229, pp. viii, 169, XL
                      S.) [Dissertation, Forschungszentrum Jülich GmbH
                      Zentralbibliothek, Verlag].
                      https://juser.fz-juelich.de/record/891865[7] Vasily
                      Tseplyaev, PhD Thesis. Unpublished.},
      month         = {Sep},
      date          = {2023-09-04},
      organization  = {CMD30 FisMat2023, Milan (Italy), 4 Sep
                       2023 - 8 Sep 2023},
      subtyp        = {After Call},
      cin          = {IAS-1 / PGI-1},
      cid          = {I:(DE-Juel1)IAS-1-20090406 / I:(DE-Juel1)PGI-1-20110106},
      pnm          = {5211 - Topological Matter (POF4-521) / HDS LEE - Helmholtz
                      School for Data Science in Life, Earth and Energy (HDS LEE)
                      (HDS-LEE-20190612)},
      pid          = {G:(DE-HGF)POF4-5211 / G:(DE-Juel1)HDS-LEE-20190612},
      typ          = {PUB:(DE-HGF)6},
      doi          = {10.34734/FZJ-2023-03694},
      url          = {https://juser.fz-juelich.de/record/1016528},
}