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@INPROCEEDINGS{Wasmer:1031807,
      author       = {Wasmer, Johannes and Mozumder, Rubel},
      othercontributors = {Antognini Silva, David and Rüssmann, Philipp and Blügel,
                          Stefan},
      title        = {{P}rediction of the magnetic exchange interaction in doped
                      topological insulators},
      school       = {RWTH Aachen},
      reportid     = {FZJ-2024-05827},
      year         = {2024},
      abstract     = {We present a benchmark study of surrogate models for
                      impurities embedded into crystalline solids. Using the
                      Korringa-Kohn-Rostoker Green Function method and the AiiDA
                      workflow engine [1], we have built a database of magnetic
                      transition metal impurity dimers embedded in the topological
                      insulator Bi2Te3. We predict isotropic exchange interaction
                      of the impurity dimer in the classical Heisenberg model with
                      machine learning and then use these surrogates as input for
                      spin dynamics calculations to find the magnetic ground state
                      of the material [2]. The study compares various recent
                      E(3)-equivariant models such as ACE and MACE [3] in terms of
                      performance and reproducible end-to-end
                      workflows.References.[1] P. Rüßmann, F. Bertoldo, S.
                      Blügel, npj. Comput. Mater., 7, 13 (2021)[2] P. Rüßmann,
                      J. Ribas Sobreviela, M. Sallermann, M. Hoffmann, F. Rhiem,
                      S. Blügel, Front. Mater., 9, (2022)[3] Batatia, I.,
                      Kovács, D. P., Simm, G. N. C., Ortner, C. $\&$ Csányi, G.
                      MACE: Higher Order Equivariant Message Passing Neural
                      Networks for Fast and Accurate Force Fields. Preprint
                      (2022).},
      month         = {Jul},
      date          = {2024-07-08},
      organization  = {Machine Learning of First Principles
                       Observables, Berlin (Germany), 8 Jul
                       2024 - 12 Jul 2024},
      subtyp        = {Invited},
      cin          = {PGI-1},
      cid          = {I:(DE-Juel1)PGI-1-20110106},
      pnm          = {5211 - Topological Matter (POF4-521) / 5111 -
                      Domain-Specific Simulation $\&$ Data Life Cycle Labs (SDLs)
                      and Research Groups (POF4-511) / AIDAS - Joint Virtual
                      Laboratory for AI, Data Analytics and Scalable Simulation
                      $(aidas_20200731)$ / HDS LEE - Helmholtz School for Data
                      Science in Life, Earth and Energy (HDS LEE)
                      (HDS-LEE-20190612) / DFG project G:(GEPRIS)390534769 - EXC
                      2004: Materie und Licht für Quanteninformation (ML4Q)
                      (390534769)},
      pid          = {G:(DE-HGF)POF4-5211 / G:(DE-HGF)POF4-5111 /
                      $G:(DE-Juel-1)aidas_20200731$ / G:(DE-Juel1)HDS-LEE-20190612
                      / G:(GEPRIS)390534769},
      typ          = {PUB:(DE-HGF)6},
      doi          = {10.34734/FZJ-2024-05827},
      url          = {https://juser.fz-juelich.de/record/1031807},
}