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@INPROCEEDINGS{Hilgers:1008352,
      author       = {Hilgers, Robin and Wortmann, Daniel and Blügel, Stefan},
      title        = {{A}pplication of {M}achine-{L}earning {M}odels and {XAI} in
                      {M}aterials {S}cience {U}sing {M}agnetic {DFT} {D}ata},
      reportid     = {FZJ-2023-02298},
      year         = {2023},
      abstract     = {The prediction of material properties using ab-initio
                      simulations [1] is a research field of fundamental
                      significance due to the multitude of materials applications
                      in technology and research. Such simulations can require
                      huge computational resources if carried out for many
                      materials, and thus the possibility to employ machine
                      learning methods offers potential benefit by restricting the
                      materials search space.In our work, we demonstrate this idea
                      for the particular field of ordered and disordered magnetic
                      Heusler alloys. In detail, we focus on
                      theirCurie-temperature, which often is the major quantity
                      describing the fitness of a magnetic material for
                      application. Thus, we studied the possibility to predict the
                      Curie-temperature and thereby classifying the materials
                      relevance for possible technical application using
                      machine-learning algorithms.We compared the performance of
                      regression models and classification models in order to
                      predict the range of the Curie-temperature of given
                      compounds to demonstrate the possibility to reduce the
                      computational expanses of simulating this quantity. This
                      work can be seen as a small-scale case study in which
                      lightweight machine learning algorithms can add value to
                      existing simulation data and eventually replace costly
                      computational steps in layered calculation workflows in the
                      future. In addition, we demonstrate, that such models can
                      lead to interesting unbiased physical insight.
                      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.[1] R. Kovacik, P.
                      Mavropoulos, S. Blüugel, Materials Cloud (2022)
                      10.24435/MATERIALSCLOUD:WW-PV},
      month         = {Jun},
      date          = {2023-06-13},
      organization  = {Helmholtz.Ai, Hamburg (Germany), 13
                       Jun 2023 - 14 Jun 2023},
      subtyp        = {Other},
      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)24},
      doi          = {10.34734/FZJ-2023-02298},
      url          = {https://juser.fz-juelich.de/record/1008352},
}