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@ARTICLE{Bosoni:1018070,
      author       = {Bosoni, Emanuele and Beal, Louis and Bercx, Marnik and
                      Blaha, Peter and Blügel, Stefan and Bröder, Jens and
                      Callsen, Martin and Cottenier, Stefaan and Degomme, Augustin
                      and Dikan, Vladimir and Eimre, Kristjan and Flage-Larsen,
                      Espen and Fornari, Marco and Garcia, Alberto and Genovese,
                      Luigi and Giantomassi, Matteo and Huber, Sebastiaan P. and
                      Janssen, Henning and Kastlunger, Georg and Krack, Matthias
                      and Kresse, Georg and Kühne, Thomas D. and Lejaeghere, Kurt
                      and Madsen, Georg K. H. and Marsman, Martijn and Marzari,
                      Nicola and Michalicek, Gregor and Mirhosseini, Hossein and
                      Müller, Tiziano M. A. and Petretto, Guido and Pickard,
                      Chris J. and Poncé, Samuel and Rignanese, Gian-Marco and
                      Rubel, Oleg and Ruh, Thomas and Sluydts, Michael and
                      Vanpoucke, Danny E. P. and Vijay, Sudarshan and Wolloch,
                      Michael and Wortmann, Daniel and Yakutovich, Aliaksandr V.
                      and Yu, Jusong and Zadoks, Austin and Zhu, Bonan and Pizzi,
                      Giovanni},
      title        = {{H}ow to verify the precision of density-functional-theory
                      implementations via reproducible and universal workflows},
      journal      = {Nature reviews / Physics},
      volume       = {6},
      issn         = {2522-5820},
      address      = {London},
      publisher    = {Springer Nature},
      reportid     = {FZJ-2023-04521},
      pages        = {45-58},
      year         = {2024},
      abstract     = {Density-functional theory methods and codes adopting
                      periodic boundary conditions are extensively used in
                      condensed matter physics and materials science research. In
                      2016, their precision (how well properties computed with
                      different codes agree among each other) was systematically
                      assessed on elemental crystals: a first crucial step to
                      evaluate the reliability of such computations. In this
                      Expert Recommendation, we discuss recommendations for
                      verification studies aiming at further testing precision and
                      transferability of density-functional-theory computational
                      approaches and codes. We illustrate such recommendations
                      using a greatly expanded protocol covering the whole
                      periodic table from Z = 1 to 96 and characterizing 10
                      prototypical cubic compounds for each element: four unaries
                      and six oxides, spanning a wide range of coordination
                      numbers and oxidation states. The primary outcome is a
                      reference dataset of 960 equations of state cross-checked
                      between two all-electron codes, then used to verify and
                      improve nine pseudopotential-based approaches. Finally, we
                      discuss the extent to which the current results for total
                      energies can be reused for different goals.},
      cin          = {PGI-1 / IAS-1 / JARA-FIT / JARA-HPC / IAS-9},
      ddc          = {530},
      cid          = {I:(DE-Juel1)PGI-1-20110106 / I:(DE-Juel1)IAS-1-20090406 /
                      $I:(DE-82)080009_20140620$ / $I:(DE-82)080012_20140620$ /
                      I:(DE-Juel1)IAS-9-20201008},
      pnm          = {5211 - Topological Matter (POF4-521) / 5111 -
                      Domain-Specific Simulation $\&$ Data Life Cycle Labs (SDLs)
                      and Research Groups (POF4-511) / MaX - MAterials design at
                      the eXascale. European Centre of Excellence in materials
                      modelling, simulations, and design (824143) / Helmholtz
                      Platform for Research Software Engineering - Preparatory
                      Study $(HiRSE_PS-20220812)$ / AIDAS - Joint Virtual
                      Laboratory for AI, Data Analytics and Scalable Simulation
                      $(aidas_20200731)$},
      pid          = {G:(DE-HGF)POF4-5211 / G:(DE-HGF)POF4-5111 /
                      G:(EU-Grant)824143 / $G:(DE-Juel-1)HiRSE_PS-20220812$ /
                      $G:(DE-Juel-1)aidas_20200731$},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:001103174800001},
      doi          = {10.1038/s42254-023-00655-3},
      url          = {https://juser.fz-juelich.de/record/1018070},
}