Making atomistic materials calculations accessible with the AiiDAlab Quantum ESPRESSO app
Despite the wide availability of density functional theory (DFT) codes, their adoption by the broader materials science community remains limited due to challenges such as software installation, input preparation, high-performance computing setup, and output analysis. To overcome these barriers, we introduce the Quantum ESPRESSO app, an intuitive, web-based platform built on AiiDAlab that integrates user-friendly graphical interfaces with automated DFT workflows. The app employs a modular Input-Process-Output model and a plugin-based architecture, providing predefined computational protocols, automated error handling, and interactive results visualization. We demonstrate the app's capabilities through plugins for electronic band structures, projected density of states, phonon, infrared/Raman, X-ray and muon spectroscopies, Hubbard parameters (DFT+<i>U</i>+<i>V</i>), Wannier functions, and post-processing tools. By extending the FAIR principles to simulations, workflows, and analyses, the app enhances the accessibility and reproducibility of advanced DFT calculations and provides a general template to interface with other first-principles calculation codes.
Authors
- Peter N O Gillespie
- Lorenzo Bastonero (ORCID: https://orcid.org/0000-0001-9374-1876)
- Ifeanyi John Onuorah (ORCID: https://orcid.org/0000-0003-0448-1297)
- Sebastiaan P. Huber (ORCID: https://orcid.org/0000-0001-5845-8880)
- Aliaksandr V. Yakutovich (ORCID: https://orcid.org/0000-0002-7496-3075)
- Edan Bainglass (ORCID: https://orcid.org/0000-0001-7218-0176)
- Deborah Prezzi (ORCID: https://orcid.org/0000-0002-7294-7450)
- Elisa Molinari (ORCID: https://orcid.org/0000-0002-0692-6096)
- Carlo A. Pignedoli (ORCID: https://orcid.org/0000-0002-8273-6390)
- R. De Renzi (ORCID: https://orcid.org/0000-0002-5015-0061)
- Daniel Hollas (ORCID: https://orcid.org/0000-0003-4075-6438)
- Iurii Timrov (ORCID: https://orcid.org/0000-0002-6531-9966)
- Timo Reents
- Jusong Yu
- Michael A. Hernández-Bertrán
- Nataliya Paulish (ORCID: https://orcid.org/0009-0001-0311-2169)
- Pietro Bonfà
- Giovanni Pizzi
- Nicola Marzari
- Junfeng Qiao
- Christopher J. Sewell
- Miki Bonacci
- Marnik Bercx
- Dou Du
- Xing WANG
- Andres Ortega-Guerrero
Institutions
- University of Modena and Reggio Emilia (IT)
- University of Parma (IT)
- University of Bremen (DE)
- Paul Scherrer Institute (CH)
- University of Bristol (GB)
- Istituto Nanoscienze (IT)
- École Polytechnique Fédérale de Lausanne (CH)
- Swiss Federal Laboratories for Materials Science and Technology (CH)
Publication Details
- Journal
- npj Computational Materials
- Published
- 2026-02-03
- DOI
- https://doi.org/10.1038/s41524-025-01936-4
- Citations
- 1
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 16.08
Funders
- National Science Foundation
- UK Research and Innovation
- European Commission
- Deutsche Forschungsgemeinschaft
- École Polytechnique Fédérale de Lausanne
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
- Università degli Studi di Parma
- Universität Bremen
- Board of the Swiss Federal Institutes of Technology
- Centro Svizzero di Calcolo Scientifico
- National Supercomputing Centre Singapore
- Horizon 2020 Framework Programme
- Engineering and Physical Sciences Research Council
- Staatssekretariat für Bildung, Forschung und Innovation
- National Center of Competence in Research Materials’ Revolution: Computational Design and Discovery of Novel Materials
- National Supercomputing Center, Korea Institute of Science and Technology Information