% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@INPROCEEDINGS{Wasmer:1020052,
      author       = {Wasmer, Johannes and Rüssmann, Philipp and Blügel,
                      Stefan},
      title        = {{C}omparison of structural representations for machine
                      learning-enhanced {DFT} of impurity embeddings},
      school       = {RWTH Aachen University},
      reportid     = {FZJ-2023-05853},
      year         = {2022},
      abstract     = {The acceleration or even replacement of ab initio methods
                      for atomistic systems with surrogate models based on machine
                      learning has gained traction in recent years [1]. This
                      development stands on two pillars: The first one is the fast
                      growth of materials databases, thanks in part to
                      high-throughput calculation (HTC) infrastructures such as
                      AiiDA [2]. The second one is advances in method development
                      in atomistic machine learning, where finding the best
                      representation of an atomic system as input for model
                      training has been identified as a crucial step to success.
                      Structural representations rely, like the Schrödinger
                      equation, only on the atom positions and their chemical
                      identity within a system [3], and are thus most suitable for
                      this task.Here we investigate the possibility to accelerate
                      the density functional theory (DFT) code juKKR [4] with
                      machine learning starting potentials. This code has been
                      used for instance to perform HTC on impurity embeddings into
                      topological insulators [5]. We use a combinatorial approach
                      to generate 7000 impurity embeddings from most elements of
                      the periodic table into elemental crystals with the help of
                      AiiDA. We generate their fingerprints using structural
                      descriptors implemented in the DScribe package [6], such as
                      smooth overlap of atomic positions. To benchmark their
                      representational power for these embeddings, we present the
                      results of a simple classification experiment.We acknowledge
                      support by the Joint Lab Virtual Materials Design (JL-VMD)
                      and thank for computing time granted by the JARA
                      Vergabegremium and provided on the JARA Partition part of
                      the supercomputer CLAIX at RWTH Aachen University. This work
                      was funded by the Deutsche Forschungsgemeinschaft (DFG,
                      German Research Foundation) under Germany's Excellence
                      Strategy – Cluster of Excellence Matter and Light for
                      Quantum Computing (ML4Q) EXC 2004/1 – 390534769, and by
                      AIDAS2 – AI, Data Analytics and Scalable Simulation – a
                      virtual lab between CEA, France and FZJ, Germany.},
      month         = {Jul},
      date          = {2021-07-20},
      organization  = {Virtual Materials Design 2021, online
                       (Germany), 20 Jul 2021 - 21 Jul 2021},
      subtyp        = {After Call},
      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)24},
      doi          = {10.34734/FZJ-2023-05853},
      url          = {https://juser.fz-juelich.de/record/1020052},
}