How to verify the precision of density-functional-theory implementations via reproducible and universal workflows

Emanuele Bosoni, Louis Beal, Marnik Bercx, Peter Blaha, Stefan Blügel, Jens Bröder, Martin Callsen, Stefaan Cottenier, Augustin Degomme, Vladimir Dikan, Kristjan Eimre, Espen Flage-Larsen, Marco Fornari, Alberto Garcia, Luigi Genovese, Matteo Giantomassi, Sebastian P. Huber, Henning Janssen, Georg Kastlunger, Matthias KrackGeorg Kresse, Thomas D. Kühne, Kurt Lejaeghere, Georg K. H. Madsen, Martijn Marsman, Nicola Marzari, Gregor Michalicek, Hossein Mirhosseini, Tiziano M. A. Müller, Guido Petretto, Chris J. Pickard, Samuel Poncé, Gian-Marco Rignanese, Oleg Rubel, Thomas Ruh, Michael Sluydts, Danny E. P. Vanpoucke, Sudarshan Vijay, Michael Wolloch, Daniel Wortmann, AliaksandrV. Yakutovich, Jusong Yu, Austin Zadoks, Bonan Zhu, Giovanni Pizzi

Publikation: Beitrag in FachzeitschriftArtikelForschungBegutachtung

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.
OriginalspracheEnglisch
Seitenumfang14
FachzeitschriftNature Reviews. Physics (e-only)
Jahrgang2023
Ausgabenummer??? Stand: 27. November 2023
DOIs
PublikationsstatusVeröffentlicht - 14 Nov. 2023

Bibliographische Notiz

Funding Information:
This work was inspired and is supported in part by the European Union (EU)’s Horizon 2020 research and innovation programme under grant agreements no. 676598 and no. 824143 (European MaX Centre of Excellence ‘Materials Design at the Exascale’) and by NCCR MARVEL, a National Centre of Competence in Research, funded by the Swiss National Science Foundation (SNSF, grant no. 205602). For the purpose of Open Access, a CC BY public copyright licence is applied to any Author Accepted Manuscript (AAM) version arising from this submission. We thank F. J. dos Santos for discussions on the analysis of the smearing types and k-point convergence, and X. Gonze, M. Torrent and F. Jollet for discussions on PAW pseudopotentials. M.F. and N.M. acknowledge the contribution of S. Shankar in early discussions about the use of prototype oxides as general platform to explore the transferability of pseudopotentials. Work at ICMAB (E.B., A.G., V.D.) is supported by the Severo Ochoa Centers of Excellence Program (MCIN CEX2019-000917-S), by grant PGC2018-096955-B-C44 of MCIN/AEI/10.13039/501100011033, ‘ERDF A way of making Europe’, and by GenCat 2017SGR1506. We also thank the Barcelona Supercomputer Center (BSC) for computational resources. V.D. acknowledges support from DOC-FAM, EU Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement no. 754397. O.R. acknowledges travel support from WIEN2k (Technical University of Vienna). The Jülich team (S.B., J.B., H.J., G.M., D.W.) acknowledge support by the Joint Lab Virtual Materials Design of the Forschungszentrum Jülich, the Helmholtz Platform for Research Software Engineering — Preparatory Study, the Joint Virtual Laboratory AI, Data Analytics and Scalable Simulation of the Forschungszentrum Jülich and the French Alternative Energies and Atomic Energy Commission, and the computing time granted through JARA on the supercomputers JURECA at Forschungszentrum Jülich and CLAIX at RWTH Aachen University. H.M. and T.D.K. (Univ. Paderborn) acknowledge the Gauss Centre for Supercomputing e.V. (www.gauss-centre.eu) for funding this project by providing computing time on the GCS supercomputer JUWELS at Jülich Supercomputing Centre. S.P. and G.-M.R. (Univ. catholique de Louvain) acknowledge support from the Fonds de la Recherche Scientifique de Belgique (F.R.S.-FNRS). Computational resources have been provided by the PRACE-21 resources MareNostrum at the BSC-CNS and by the Consortium des Équipements de Calcul Intensif, funded by the F.R.S.-FNRS under grant no. 2.5020.11 and by the Walloon Region as well as computational resources awarded on the Belgian share of the EuroHPC LUMI supercomputer. G.Ka. and S.V. received funding from the VILLUM Centre for the Science of Sustainable Fuels and Chemicals (9455) from VILLUM FONDEN. Computational resources were provided by the Niflheim supercomputing cluster at the Technical University of Denmark. They also thank J. J. Mortensen and A. H. Larsen for discussions on optimizing the workflow for the GPAW code. S.C. acknowledges financial support from OCAS NV by an OCAS-endowed chair at Ghent University. The computational resources and services used at Ghent University were provided by the Vienna Scientific Cluster (VSC; Flemish Supercomputer Center), funded by the Research Foundation Flanders and the Flemish Government department EWI. M.W. acknowledges computational resources provided by the VSC. This research was funded in part by the Austrian Science Fund (FWF) [P 32711]. E.F.L. acknowledges resources provided by Sigma2 — the National Infrastructure for High Performance Computing and Data Storage in Norway, and support from the Norwegian Research Infrastructure Services. B.Z. is grateful to the UK Materials and Molecular Modelling Hub for computational resources, which is partially funded by EPSRC (EP/P020194/1 and EP/T022213/1), and acknowledges the use of the UCL Myriad and Kathleen High Performance Computing Facility (Myriad@UCL, Kathleen@UCL), and associated support services, in the completion of this work. N.M., G.Pi. and A.G. acknowledge support from the EU Horizon 2020 research and innovation programme under grant agreement no. 957189 (BIG-MAP), also part of the BATTERY 2030+ initiative under grant agreement no. 957213. G.P., J.Y. and G.-M.R. acknowledge support by the SNSF and by the F.R.S.-FNRS through the ‘FISH4DIET’ Project (SNSF grant 200021E_206190 and F.R.S.-FNRS grant T.0179.22). G.P. acknowledges support by the Open Research Data Program of the ETH Board, under the Establish project ‘PREMISE’. J.Y. acknowledges support from the EU Horizon 2020 research and innovation programme under grant agreement no. 760173 (MARKETPLACE).

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