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@ARTICLE{Hilgers:1018604,
      author       = {Hilgers, Robin and Wortmann, Daniel and Blügel, Stefan},
      title        = {{M}achine {L}earning-based estimation and explainable
                      artificial intelligence-supported interpretation of the
                      critical temperature from magnetic ab initio {H}eusler
                      alloys data},
      reportid     = {FZJ-2023-04922},
      year         = {2023},
      note         = {Non-exclusive perpetual license},
      abstract     = {Machine Learning (ML) has impacted numerous areas of
                      materials science, most prominently improving molecular
                      simulations, where force fields were trained on previously
                      relaxed structures. One natural next step is to predict
                      material properties beyond structure. In this work, we
                      investigate the applicability and explainability of ML
                      methods in the use case of estimating the critical
                      temperature for magnetic Heusler alloys calculated using ab
                      initio methods determined materials-specific magnetic
                      interactions and a subsequent Monte Carlo (MC) approach. We
                      compare the performance of regression and classification
                      models to predict the range of the critical temperature of
                      given compounds without performing the MC calculations.
                      Since the MC calculation requires computational resources in
                      the same order of magnitude as the density-functional theory
                      (DFT) calculation, it would be advantageous to replace
                      either step with a less computationally intensive method
                      such as ML. We discuss the necessity to generate the
                      magnetic ab initio results to make a quantitative prediction
                      of the critical temperature. We used state-of-the-art
                      explainable artificial intelligence (XAI) methods to extract
                      physical relations and deepen our understanding of patterns
                      learned by our models from the examined data.},
      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)25},
      doi          = {10.34734/FZJ-2023-04922},
      url          = {https://juser.fz-juelich.de/record/1018604},
}