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@ARTICLE{Hilgers:1042415,
      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},
      journal      = {Electronic structure},
      volume       = {7},
      number       = {1},
      issn         = {2516-1075},
      address      = {Philadelphia, PA},
      publisher    = {IOP Publishing Ltd.},
      reportid     = {FZJ-2025-02563},
      pages        = {015005 -},
      year         = {2025},
      abstract     = {The increased availability of computing time, in recent
                      years, allows for systematic high-throughput studies of
                      material classes. Such studies serve 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 (ML)-based
                      approach to improve the input parameters optimized during a
                      density functional theory computation or workflow. This
                      approach of integrating ML 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          = {PGI-1},
      ddc          = {621.3},
      cid          = {I:(DE-Juel1)PGI-1-20110106},
      pnm          = {5211 - Topological Matter (POF4-521)},
      pid          = {G:(DE-HGF)POF4-5211},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:001438651000001},
      doi          = {10.1088/2516-1075/adbaa0},
      url          = {https://juser.fz-juelich.de/record/1042415},
}