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@ARTICLE{Hilgers:1018603,
      author       = {Hilgers, Robin and Wortmann, Daniel and Blügel, Stefan},
      title        = {{A}pplication of batch learning for boosting
                      high-throughput ab initio success rates and reducing
                      computational effort required using data-driven processes},
      publisher    = {arXiv},
      reportid     = {FZJ-2023-04921},
      year         = {2023},
      note         = {Non-exclusive perpetual license},
      abstract     = {The increased availability of computing time, in recent
                      years, allows for systematic high-throughput studies of
                      material classes with the purpose of both screening for
                      materials with remarkable properties and understanding how
                      structural configuration and material composition affect
                      macroscopic attributes manifestation. However, when
                      conducting systematic high-throughput studies, the
                      individual ab initio calculations' success depends on the
                      quality of the chosen input quantities. On a large scale,
                      improving input parameters by trial and error is neither
                      efficient nor systematic. We present a systematic,
                      high-throughput compatible, and machine learning-based
                      approach to improve the input parameters optimized during a
                      DFT computation or workflow. This approach of integrating
                      machine learning into a typical high-throughput workflow
                      demonstrates the advantages and necessary considerations for
                      a systematic study of magnetic multilayers of 3d transition
                      metal layers on FCC noble metal substrates. For 6660 film
                      systems, we were able to improve the overall success rate of
                      our high-throughput FLAPW-based structural relaxations from
                      $64.8\%$ to 94.3 $\%$ while at the same time requiring 17
                      $\%$ less computational time for each successful
                      relaxation.},
      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-2023-04921},
      url          = {https://juser.fz-juelich.de/record/1018603},
}