Published October 29, 2021 | Version v2
Dataset Open

Common workflows for computing material properties using different quantum engines

  • 1. Theory and Simulation of Materials (THEOS) and National Centre for Computational Design and Discovery of Novel Materials (MARVEL), École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland
  • 2. Institut de Ciència de Materials de Barcelona, ICMAB-CSIC, Campus UAB, 08193 Bellaterra, Spain
  • 3. Peter Grünberg Institut and Institute for Advanced Simulation, Forschungszentrum Jülich, D-52425 Jülich, Germany
  • 4. Department of Physics, RWTH Aachen University, D-52056, Aachen, Germany
  • 5. Univ. Grenoble-Alpes, CEA, IRIG-MEM-L_Sim, 38000 Grenoble, France
  • 6. nanotech@surfaces laboratory, Swiss Federal Laboratories for Materials Science and Technology (Empa), CH-8600 Dübendorf, Switzerland
  • 7. SINTEF Industry, Materials Physics, Oslo, Norway
  • 8. University of Oslo, Department of Physics, Norway
  • 9. Microsoft Station Q, University of California, Santa Barbara, California, 93106-6105, USA
  • 10. Atomistic Simulation Centre, School of Mathematics and Physics, Queen's University Belfast, United Kingdom
  • 11. UCLouvain, Institut de la Matière Condensée et des Nanosciences (IMCN), Chemin des Étoiles 8, Louvain-la-Neuve 1348, Belgium
  • 12. Laboratory of Molecular Simulation (LSMO), Institut des sciences et ingénierie chimiques (ISIC), École Polytechnique Fédérale de Lausanne (EPFL) Valais, CH-1951, Sion, Switzerland
  • 13. Department of Chemistry, Claverton Down, University of Bath, BA2 7AY, Bath, United Kingdom
  • 14. The Faraday Institution, Didcot OX11 0RA, United Kingdom
  • 15. Department of Chemistry, University College London, 20 Gordon St, Bloomsbury, London WC1H 0AJ, United Kingdom

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Description

The prediction of material properties through electronic-structure simulations based on density-functional theory has become routinely common, thanks, in part, to the steady increase in the number and robustness of available simulation packages. This plurality of codes and methods aiming to solve similar problems is both a boon and a burden. While providing great opportunities for cross-verification, these packages adopt different methods, algorithms, and paradigms, making it challenging to choose, master, and efficiently use any one for a given task. Leveraging recent advances in managing reproducible scientific workflows, we demonstrate how developing common interfaces for workflows that automatically compute material properties can tackle the challenge mentioned above, greatly simplifying interoperability and cross-verification. We introduce design rules for reproducible and reusable code-agnostic workflow interfaces to compute well-defined material properties, which we implement for eleven different quantum engines and use to compute three different material properties. Each implementation encodes carefully selected simulation parameters and workflow logic, making the implementer's expertise of the quantum engine directly available to non-experts. Full provenance and reproducibility of the workflows is guaranteed through the use of the AiiDA infrastructure. All workflows are made available as open-source and come pre-installed with the Quantum Mobile virtual machine, making their use straightforward. This entry contains all data and scripts to reproduce the figures of the corresponding scientific paper.

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References

Preprint
Sebastiaan P. Huber, Emanuele Bosoni, Marnik Bercx, Jens Bröder, Augustin Degomme, Vladimir Dikan, Kristjan Eimre, Espen Flage-Larsen, Alberto Garcia, Luigi Genovese, Dominik Gresch, Conrad Johnston, Guido Petretto, Samuel Poncé, Gian-Marco Rignanese, Christopher J. Sewell, Berend Smit, Vasily Tseplyaev, Martin Uhrin, Daniel Wortmann, Aliaksandr V. Yakutovich, Austin Zadoks, Pezhman Zarabadi-Poor, Bonan Zhu, Nicola Marzari, Giovanni Pizzi, Common workflows for computing material properties using different quantum engines (2021), arXiv:2105.05063 [cond-mat.mtrl-sci].

Journal reference
Huber, S.P., Bosoni, E., Bercx, M. et al. Common workflows for computing material properties using different quantum engines. npj Comput Mater 7, 136 (2021)., doi: 10.1038/s41524-021-00594-6