Sebastiaan P. Huber^1*, Emanuele Bosoni², Marnik Bercx¹, Jens Bröder^3,4, Augustin Degomme⁵, Vladimir Dikan², Kristjan Eimre⁶, Espen Flage-Larsen^7,8, Alberto Garcia², Luigi Genovese⁵, Dominik Gresch⁹, Conrad Johnston¹⁰, Guido Petretto¹¹, Samuel Poncé¹, Gian-Marco Rignanese¹¹, Christopher J. Sewell¹, Berend Smit¹², Vasily Tseplyaev^3,4, Martin Uhrin¹, Daniel Wortmann³, Aliaksandr V. Yakutovich^12,1, Austin Zadoks¹, Pezhman Zarabadi-Poor^13,14, Bonan Zhu^15,14, Nicola Marzari¹, Giovanni Pizzi^1*

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

* Corresponding authors emails: mail@sphuber.net, giovanni.pizzi@epfl.ch

DOI10.24435/materialscloud:2a-yh [version v2]

Publication date: Oct 29, 2021

How to cite this record

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, Materials Cloud Archive 2021.181 (2021), https://doi.org/10.24435/materialscloud:2a-yh

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.

Materials Cloud sections using this data

Files

File name	Size	Description
README.md MD5md5:25735886ad19ff420de39d716fb5f02c	7.4 KiB	A file describing the contents of the `aiida-common-workflows.zip` file and instructions on how to reproduce the plots as well as instructions on recreating the data from the `aiida-common-workflows.aiida` AiiDA export archive.
aiida-common-workflows.aiida MD5md5:fdd5aebd182001985b93cc8251bcf940 Open this AiiDA archive on renkulab.io (https://renkulab.io/)	1.2 GiB	An AiiDA export archive that contains all the raw data from which the curated data and plots have been generated.
aiida-common-workflows.zip MD5md5:4f5e674fdd5a94d51895473c7d9b471a	51.2 KiB	A zip file that contains all the scripts necessary to reproduce the curated data from the provided AiiDA archive and reproduce the plots from that curated data.

License

Files and data are licensed under the terms of the following license: Creative Commons Attribution 4.0 International.
Metadata, except for email addresses, are licensed under the Creative Commons Attribution Share-Alike 4.0 International license.

External references

Keywords

DFT workflows automated AiiDA common interfaces verification MARVEL/OSP MaX SNSF H2020 Intersect