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@MISC{Hilgers:1017779,
      author       = {Hilgers, Robin and Wortmann, Daniel and Blügel, Stefan},
      title        = {{C}ode collection for machine learning assisted materials
                      screening in the search for novel half-metallic {H}eusler
                      alloys},
      reportid     = {FZJ-2023-04312},
      year         = {2023},
      note         = {MIT License, Collaboration with a group from the University
                      of Alabama},
      abstract     = {This code collection contains scripts that have been used
                      to screen the Materials Project database for novel (nearly)
                      half-metallic full (L21) and inverse (XA) Heusler alloys by
                      employing machine learning-based methods in order to predict
                      the spin-polarization of the density of states at the Fermi
                      level. The models used have been trained on
                      density-functional theory data collected by
                      collaborators.Each step of the materials screening process
                      is performed by the provided scripts, including Data
                      processing, Hyperparameter Optimization, Model Training $\&$
                      Evaluation, Screening Data (The Materials Project) Download
                      $\&$ Processing, Predictive Modeling, and Filtering of the
                      Predictions. Also, scripts compiling additional explorative
                      visualizations and SHAP-guided explainable artificial
                      intelligence visualizations are included.Upon investigation
                      of the predicted compounds, FLAPW electronic structure
                      validation computations have been performed. Calculation
                      submission, spin-polarization computation and visualization
                      scripts depicting the validation process are included in
                      this publication as well.The XGBoost model which has been
                      used by us is included (stored using the Python package
                      pickle) for future reproducibility of our results.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.},
      cin          = {IAS-1},
      cid          = {I:(DE-Juel1)IAS-1-20090406},
      pnm          = {899 - ohne Topic (POF4-899) / HDS LEE - Helmholtz School
                      for Data Science in Life, Earth and Energy (HDS LEE)
                      (HDS-LEE-20190612)},
      pid          = {G:(DE-HGF)POF4-899 / G:(DE-Juel1)HDS-LEE-20190612},
      typ          = {PUB:(DE-HGF)33},
      url          = {https://juser.fz-juelich.de/record/1017779},
}