Common workflows for computing material properties using different quantum engines

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 WortmannAliaksandr V. Yakutovich, Austin Zadoks, Pezhman Zarabadi-Poor, Bonan Zhu, Nicola Marzari, Giovanni Pizzi

Research output: Contribution to journalArticlepeer-review

10 Citations (SciVal)


The prediction of material properties 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 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 them. We demonstrate how developing common interfaces for workflows that automatically compute material properties greatly simplifies interoperability and cross-verification. We introduce design rules for reusable, code-agnostic, workflow interfaces to compute well-defined material properties, which we implement for eleven quantum engines and use to compute various 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. All workflows are made available as open-source and full reproducibility of the workflows is guaranteed through the use of the AiiDA infrastructure.

Original languageEnglish
Article number136
Journalnpj Computational Materials
Issue number1
Publication statusPublished - 19 Aug 2021

Bibliographical note

Funding Information:
This work is supported by the MARVEL National Centre of Competence in Research (NCCR) funded by the Swiss National Science Foundation (grant agreement ID 51NF40-182892) and by the European Union’s Horizon 2020 research and innovation program under Grant Agreement No. 824143 (European MaX Centre of Excellence “Materials design at the Exascale”) and Grant Agreement No. 814487 (INTERSECT project). We thank M. Giantomassi and J.-M. Beuken for their contributions in adding support for PseudoDojo tables to the aiida-pseudo ( aiidateam/aiida-pseudo) plugin. We also thank X. Gonze, M. Giantomassi, M. Probert, C. Pickard, P. Hasnip, J. Hutter, M. Iannuzzi, D. Wortmann, S. Blügel, J. Hess, F. Neese, and P. Delugas for providing useful feedback on the various quantum engine implementations. S.P. acknowledges support from the European Unions Horizon 2020 Research and Innovation Programme, under the Marie Skłodowska-Curie Grant Agreement SELPH2D No. 839217 and computer time provided by the PRACE-21 resources MareNostrum at BSC-CNS. E.F.-L. acknowledges the support of the Norwegian Research Council (project number 262339) and computational resources provided by Sigma2. P.Z.-P. thanks to the Faraday Institution CATMAT project (EP/ S003053/1, FIRG016) for financial support. KE acknowledges the Swiss National Science Foundation (grant number 200020-182015). G.Pi. and K.E. acknowledge the swissuniversities “Materials Cloud” (project number 201-003). Work at ICMAB is supported by the Severo Ochoa Centers of Excellence Program (MICINN CEX2019-000917-S), by PGC2018-096955-B-C44 (MCIU/AEI/FEDER, UE), and by GenCat 2017SGR1506. B.Z. thanks to the Faraday Institution FutureCat project (EP/S003053/ 1, FIRG017) for financial support. J.B. and V.T. acknowledge support by the Joint Lab Virtual Materials Design (JLVMD) of the Forschungszentrum Jülich.

Publisher Copyright:
© 2021, The Author(s).

ASJC Scopus subject areas

  • Modelling and Simulation
  • Materials Science(all)
  • Mechanics of Materials
  • Computer Science Applications


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