% 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:1053929,
      author       = {Wasmer, Johannes},
      collaboration = {Rüssmann, Philipp and Sanvito, Stefano and Cangi, Attila
                      and Assent, Ira},
      othercontributors = {Blügel, Stefan},
      title        = {{T}owards all-electron treatment in electronic structure
                      machine learning},
      school       = {RWTH Aachen},
      reportid     = {FZJ-2026-01614},
      year         = {2025},
      abstract     = {Electronic structure calculations with density functional
                      theory (DFT) are today the most widely used technique for
                      computational materials design. As materials design enters
                      the artificial intelligence (AI) age, large databases of
                      these calculations, such as the Materials Project, form the
                      basis for creating foundation models. This can be seen in
                      leaderboards such as Matbench Discovery [1]. These AI
                      foundation models mark a paradigm shift in computational
                      materials design since they accelerate the time to solution
                      by orders of magnitude. However, the current DFT databases
                      and AI surrogate models are not accurate enough to design
                      quantum materials. This problem can be solved by employing
                      all-electron, full-potential, fully relativistic DFT, that
                      can reach the required calculation precision. The JuDFT.de
                      codes are among the few established implementations of such
                      methods [2]. They are the only all-electron DFT codes
                      supporting high-throughput data generation that implement
                      the FAIR data guidelines via their plugin integration with
                      the AiiDA.net workflow engine.We present our progress on the
                      open problem of surrogate AI models for all-electron DFT
                      data for the JuDFT codes. We focus on the JuKKR code, and
                      materials systems with defects, such as magnetically doped
                      topological insulators [3]. We predict the converged
                      electron potential for accelerated DFT, and first-principles
                      exchange interactions for accelerated spin dynamics.[1]
                      Riebesell, J. et al. Matbench Discovery – A framework to
                      evaluate machine learning crystal stability predictions.
                      Preprint at https://doi.org/10.48550/arXiv.2308.14920
                      (2024).[2] Bosoni, E. et al. How to verify the precision of
                      density-functional-theory implementations via reproducible
                      and universal workflows. Nat Rev Phys 6, 45–58 (2024).[3]
                      Mozumder, R., Wasmer, J., Antognini Silva, D., Blügel, S.
                      $\&$ Rüßmann, P. High-throughput magnetic co-doping and
                      design of exchange interactions in topological insulators.
                      Phys. Rev. Mater. 8, 104201 (2024).},
      month         = {May},
      date          = {2025-05-27},
      organization  = {FZJ IAS Retreat 2025, Jülich
                       (Germany), 27 May 2025 - 27 May 2025},
      subtyp        = {After Call},
      cin          = {PGI-1},
      cid          = {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) / AIDAS - Joint Virtual Laboratory for
                      AI, Data Analytics and Scalable Simulation
                      $(aidas_20200731)$ / DFG project G:(GEPRIS)491111487 -
                      Open-Access-Publikationskosten / 2025 - 2027 /
                      Forschungszentrum Jülich (OAPKFZJ) (491111487)},
      pid          = {G:(DE-HGF)POF4-5211 / G:(DE-Juel1)HDS-LEE-20190612 /
                      $G:(DE-Juel-1)aidas_20200731$ / G:(GEPRIS)491111487},
      typ          = {PUB:(DE-HGF)24},
      doi          = {10.34734/FZJ-2026-01614},
      url          = {https://juser.fz-juelich.de/record/1053929},
}