Emanuele Bosoni1,
Louis Beal2,
Marnik Bercx3,
Peter Blaha4,
Stefan Blügel5,
Jens Bröder5,6,
Martin Callsen7,8,9,
Stefaan Cottenier7,8,
Augustin Degomme2,
Vladimir Dikan1,
Kristjan Eimre3,
Espen Flage-Larsen10,11,
Marco Fornari12,
Alberto Garcia1,
Luigi Genovese2,
Matteo Giantomassi13,
Sebastiaan P. Huber3,14,
Henning Janssen5,
Georg Kastlunger15,
Matthias Krack16,
Georg Kresse17,18,
Thomas D. Kühne19,20,
Kurt Lejaeghere8,21,
Georg K. H. Madsen4,
Martijn Marsman17,18,
Nicola Marzari3,16,
Gregor Michalicek5,
Hossein Mirhosseini22,
Tiziano M. A. Müller23,
Guido Petretto13,
Chris J. Pickard24,25,
Samuel Poncé13,
Gian-Marco Rignanese13,
Oleg Rubel26,
Thomas Ruh4,8,
Michael Sluydts7,8,27,
Danny E. P. Vanpoucke7,28,
Sudarshan Vijay15,
Michael Wolloch17,18,
Daniel Wortmann5,
Aliaksandr V. Yakutovich29,
Jusong Yu3,16,
Austin Zadoks3,
Bonan Zhu30,31,
Giovanni Pizzi3,16*
1 Institut de Ciència de Materials de Barcelona, ICMAB-CSIC, Campus UAB, 08193 Bellaterra, Spain
2 Univ. Grenoble-Alpes, CEA, IRIG-MEM-L Sim, 38000 Grenoble, France
3 Theory and Simulation of Materials (THEOS) and National Centre for Computational Design and Discovery of Novel Materials (MARVEL), École Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland
4 Institute for Materials Chemistry, Technical University of Vienna, Getreidemarkt 9/165-TC, A-1060 Vienna, Austria
5 Peter Grünberg Institut and Institute for Advanced Simulation, Forschungszentrum Jülich and JARA, D-52425 Jülich, Germany
6 Institute for Advanced Simulation, Materials Data Science and Informatics (IAS-9), Forschungszentrum Jülich, D-52425 Jülich, Germany
7 Department of Electromechanical, Systems and Metal Engineering, Ghent University, Belgium
8 Center for Molecular Modeling (CMM), Ghent University, Belgium
9 Institute of Atomic and Molecular Sciences, Academia Sinica, Taipei 10617, Taiwan
10 Norwegian EuroHPC Competence Center, Sigma2 AS, Norway
11 SINTEF Industry, Materials Physics, Oslo, Norway
12 Department of Physics and Science of Advanced Materials Program, Central Michigan University, Mount Pleasant, Michigan 48859, USA
13 Institut de la Matière Condensée et des Nanosciences (IMCN), Université catholique de Louvain, Chemin des Étoiles 8, Louvain-la-Neuve 1348, Belgium
14 National Centre of Competence in Research (NCCR) Catalysis, École Polytechnique Fédérale de Lausanne (EPFL), CH-1015, Lausanne, Switzerland
15 Center for Catalysis Theory (Cattheory), Department of Physics, Technical University of Denmark (DTU), 2800 Kongens Lyngby, Denmark
16 Laboratory for Materials Simulations (LMS), Paul Scherrer Institut (PSI), CH-5232 Villigen PSI, Switzerland
17 University of Vienna, Faculty of Physics and Center for Computational Materials Science, Kolingasse 14-16, A-1090 Vienna, Austria
18 VASP Software GmbH, Sensengasse 8, A-1090 Vienna, Austria
19 Center for Advanced Systems Understanding (CASUS) and Helmholtz-Zentrum Dresden-Rossendorf, D-02826 Görlitz, Germany
20 Paderborn Center for Parallel Computing (PC2) and Center for Sustainable Systems Design, University of Paderborn, D-33098 Paderborn, Germany
21 OCAS NV/ArcelorMittal Global R&D Gent, Pres. J. F. Kennedylaan 3, Zelzate B-9060, Belgium
22 Dynamics of Condensed Matter, Chair of Theoretical Chemistry, University of Paderborn, D-33098 Paderborn, Germany
23 HPE HPC EMEA Research Lab, CH-4051 Basel, Switzerland
24 Department of Materials Science & Metallurgy, University of Cambridge, 27 Charles Babbage Road, Cambridge CB3 0FS, United Kingdom
25 Advanced Institute for Materials Research, Tohoku University 2-1-1 Katahira, Aoba, Sendai, 980-8577, Japan
26 Department of Materials Science and Engineering, McMaster University, 1280 Main Street West, Hamilton, Ontario L8S 4L8, Canada
27 ePotentia, Frans van Dijckstraat 59, 2100 Deurne Antwerpen, Belgium
28 Institute for Materials Research (IMO-IMOMEC), UHasselt - Hasselt University, Belgium
29 Swiss Federal Laboratories for Materials Science and Technology (Empa), nanotech@surfaces laboratory, CH-8600 Dübendorf, Switzerland
30 Department of Chemistry, University College London, 20 Gordon St, Bloomsbury, London WC1H 0AJ, United Kingdom
31 The Faraday Institution, Didcot OX11 0RA, United Kingdom
* Corresponding authors emails:
giovanni.pizzi@psi.ch
How to cite this record
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,
Sebastiaan P. Huber,
Henning Janssen,
Georg Kastlunger,
Matthias Krack,
Georg 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,
Aliaksandr V. Yakutovich,
Jusong Yu,
Austin Zadoks,
Bonan Zhu,
Giovanni Pizzi,
How to verify the precision of density-functional-theory implementations via reproducible and universal workflows, Materials Cloud Archive
2023.81 (2023),
doi:
10.24435/materialscloud:s4-3h.
Description
In the past decades many density-functional theory methods and codes adopting periodic boundary conditions have been developed and are now extensively used in condensed matter physics and materials science research. Only in 2016, however, their precision (i.e., to which extent 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. We discuss here general 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: 4 unaries and 6 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. Such effort is facilitated by deploying AiiDA common workflows that perform automatic input parameter selection, provide identical input/output interfaces across codes, and ensure full reproducibility. Finally, we discuss the extent to which the current results for total energies can be reused for different goals (e.g., obtaining formation energies). This data entry contains all data to reproduce the results, as well as the resulting curated all-electron dataset and the scripts to generate the figures of the paper.
Materials Cloud sections using this data
External references
Preprint (Manuscript preprint)
E. Bosoni et al, How to verify the precision of density-functional-theory implementations via reproducible and universal workflows, arXiv:2305.17274 (2023)
doi:10.48550/arXiv.2305.17274
Website (Interactive visualization of the data generated in the paper)
Keywords
DFT
verification
pseudopotentials
automation
equation of state
MARVEL/P3
