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@MASTERSTHESIS{Wasmer:1020053,
      author       = {Wasmer, Johannes},
      othercontributors = {Blügel, Stefan and Rüssmann, Philipp and Berkels,
                          Benjamin},
      title        = {{D}evelopment of a surrogate machine learning model for the
                      acceleration of density functional calculations with the
                      {K}orringa-{K}ohn-{R}ostoker method},
      school       = {RWTH Aachen University},
      type         = {Masterarbeit},
      reportid     = {FZJ-2023-05854},
      pages        = {113},
      year         = {2021},
      note         = {https://iffgit.fz-juelich.de/phd-project-wasmer/projects/single-impurity-database;
                      Masterarbeit, RWTH Aachen University, 2022},
      abstract     = {Density functional theory (DFT) has become an indispensable
                      tool in materials science. Specialized DFT methods like the
                      Korringa-Kohan Rostoker Green Function (KKR) method are
                      predestined to investigate the technologically relevant
                      effects of crystallographic defects on the electronic and
                      magnetic structure of host materials. This thesis lays the
                      groundwork for answering the question of whether surrogate
                      machine learning (ML) models have the potential to
                      accelerate such DFT calculations since their computational
                      complexity severely limits them to systems sizes of about a
                      thousand atoms in practice. To that end, a versatile suite
                      of software tools that facilitates the generation and
                      analysis of high-throughput computing DFT datasets with the
                      JuKKR DFT codes and the AiiDA workflow engine is presented.
                      We demonstrate its use by generating a database of 8,760
                      converged KKR DFT calculations of single impurity embeddings
                      into elemental crystals with 60 different chemical elements
                      and varying lattice constants and that preserves the full
                      data provenance of each calculation. Finally, we use the
                      single-impurity database to compare the Coulomb Matrix and
                      the Smooth Overlap of Atomic Positions (SOAP) as structural
                      descriptors of the local atomic environment for materials
                      defects. Their potential use in surrogate ML models is
                      showcased in a simple example of host crystal structure
                      prediction that achieves 93 percent accuracy.},
      cin          = {IAS-1 / PGI-1},
      cid          = {I:(DE-Juel1)IAS-1-20090406 / I:(DE-Juel1)PGI-1-20110106},
      pnm          = {5211 - Topological Matter (POF4-521) / DFG project
                      390534769 - EXC 2004: Materie und Licht für
                      Quanteninformation (ML4Q) (390534769) / EXC 2004:  Matter
                      and Light for Quantum Computing (ML4Q) (390534769) / AIDAS -
                      Joint Virtual Laboratory for AI, Data Analytics and Scalable
                      Simulation $(aidas_20200731)$},
      pid          = {G:(DE-HGF)POF4-5211 / G:(GEPRIS)390534769 /
                      G:(BMBF)390534769 / $G:(DE-Juel-1)aidas_20200731$},
      typ          = {PUB:(DE-HGF)19},
      doi          = {10.34734/FZJ-2023-05854},
      url          = {https://juser.fz-juelich.de/record/1020053},
}