journal article·2026-02-03Open access

Making atomistic materials calculations accessible with the AiiDAlab Quantum ESPRESSO app

Xing Wang, Edan Bainglass, Miki Bonacci, Andres Ortega-Guerrero, Lorenzo Bastonero, Marnik Bercx, Pietro Bonfà, Roberto De Renzi, Dou Du, Peter N. O. Gillespie, Michael A. Hernández-Bertrán, Daniel Hollas, Sebastiaan P. Huber, Elisa Molinari, Ifeanyi J. Onuorah, Nataliya Paulish, Deborah Prezzi, Junfeng Qiao, Timo Reents, Christopher J. Sewell, Iurii Timrov, Aliaksandr V. Yakutovich, Jusong Yu, Nicola Marzari, Carlo A. Pignedoli, Giovanni Pizzi

npj Computational Materials· Volume 12· Issue 1

Abstract

Abstract 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+ U + V ), 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.
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Research context

OpenAlex topics: Machine Learning in Materials Science · Advanced Electron Microscopy Techniques and Applications · Advanced Chemical Physics Studies

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References (100+)

Show source-provided references
1. Hohenberg, P. & Kohn, W. Inhomogeneous electron gas. Phys. Rev. 136, B864–B871 (1964).10.1103/PhysRev.136.B864 ↗
2. Kohn, W. & Sham, L. J. Self-consistent equations including exchange and correlation effects. Phys. Rev. 140, A1133 (1965).10.1103/PhysRev.140.A1133 ↗
3. Marzari, N., Ferretti, A. & Wolverton, C. Electronic-structure methods for materials design. Nat. Mater. 20, 736–749 (2021).10.1038/s41563-021-01013-3 ↗
4. Talirz, L., Ghiringhelli, L. M. & Smit, B. Trends in atomistic simulation software usage [article v1.0]. Living J. Comput. Mol. Sci. 3, 1483 (2021).10.33011/livecoms.3.1.1483 ↗
5. Lejaeghere, K. et al. Reproducibility in density functional theory calculations of solids. Science 351, aad3000 (2016).10.1126/science.aad3000 ↗
6. Bosoni, E. et al. How to verify the precision of density-functional-theory implementations via reproducible and universal workflows. Nat. Rev. Phys. 6, 45–58 (2024).10.1038/s42254-023-00655-3 ↗
7. Blum, V. et al. Roadmap on methods and software for electronic structure based simulations in chemistry and materials. Electron. Struct. 6, 042501 (2024).10.1088/2516-1075/ad48ec ↗
8. Jain, A. et al. Commentary: the materials project: a materials genome approach to accelerating materials innovation. APL Mater. 1, 011002 (2013).10.1063/1.4812323 ↗
9. Curtarolo, S. et al. The high-throughput highway to computational materials design. Nat. Mater. 12, 191–201 (2013).10.1038/nmat3568 ↗
10. Cheon, G. et al. Data mining for new two- and one-dimensional weakly bonded solids and lattice-commensurate heterostructures. Nano Lett. 17, 1915–1923 (2017).10.1021/acs.nanolett.6b05229 ↗
11. Mounet, N. et al. Two-dimensional materials from high-throughput computational exfoliation of experimentally known compounds. Nat. Nanotechnol. 13, 246–252 (2018).10.1038/s41565-017-0035-5 ↗
12. Mounet, N. et al. Two-dimensional materials from high-throughput computational exfoliation of experimentally known compounds. Mater. Cloud Archive 157, https://doi.org/10.24435/materialscloud:yf-kf (2024).10.24435/materialscloud:yf-kf ↗
13. Vecchio, K. S., Dippo, O. F., Kaufmann, K. R. & Liu, X. High-throughput rapid experimental alloy development (HT-READ). Acta Materialia 221, 117352 (2021).10.1016/j.actamat.2021.117352 ↗
14. Gjerding, M. N. et al. Recent progress of the computational 2D materials database (C2DB). 2D Mater. 8, 044002 (2021).10.1088/2053-1583/ac1059 ↗
15. Huber, S. et al. Materials Cloud three-dimensional crystals database (MC3D). Materials Cloud Archive 38, https://doi.org/10.24435/materialscloud:rw-t0 (2022).10.24435/materialscloud:rw-t0 ↗
16. Curtarolo, S. et al. AFLOW: An automatic framework for high-throughput materials discovery. Comput. Mater. Sci. 58, 218–226 (2012).10.1016/j.commatsci.2012.02.005 ↗
17. Pizzi, G., Cepellotti, A., Sabatini, R., Marzari, N. & Kozinsky, B. AiiDA: automated interactive infrastructure and database for computational science. Comput. Mater. Sci. 111, 218–230 (2016).10.1016/j.commatsci.2015.09.013 ↗
18. Uhrin, M., Huber, S. P., Yu, J., Marzari, N. & Pizzi, G. Workflows in AiiDA: engineering a high-throughput, event-based engine for robust and modular computational workflows. Comput. Mater. Sci. 187, 110086 (2021).10.1016/j.commatsci.2020.110086 ↗
19. Huber, S. P. et al. AiiDA 1.0, a scalable computational infrastructure for automated reproducible workflows and data provenance. Sci. Data 7, 300 (2020).10.1038/s41597-020-00638-4 ↗
20. Gjerding, M. et al. Atomic Simulation Recipes: A Python framework and library for automated workflows. Comput. Mater. Sci. 199, 110731 (2021).10.1016/j.commatsci.2021.110731 ↗
21. Mortensen, J. J., Gjerding, M. & Thygesen, K. S. MyQueue: task and workflow scheduling system. J. Open Source Softw. 5, 1844 (2020).10.21105/joss.01844 ↗
22. Mathew, K. et al. Atomate: A high-level interface to generate, execute, and analyze computational materials science workflows. Comput. Mater. Sci. 139, 140–152 (2017).10.1016/j.commatsci.2017.07.030 ↗
23. Jain, A. et al. Fireworks: a dynamic workflow system designed for high-throughput applications. Concurrency Comput. Pract. Exp. 27, 5037–5059 (2015).10.1002/cpe.3505 ↗
24. Cunningham, W. et al. Agnostiqhq/covalent: v0.240.0 https://doi.org/10.5281/zenodo.15400489 (2025).10.5281/zenodo.15400489 ↗
25. Rosen, A. S. et al. Jobflow: computational workflows made simple. J. Open Source Softw. 9, 5995 (2024).10.21105/joss.05995 ↗
26. Armiento, R. Database-Driven High-Throughput Calculations and Machine Learning Models for Materials Design. In Schütt, K. T. et al. (eds.) Machine Learning Meets Quantum Physics, 377–395 https://doi.org/10.1007/978-3-030-40245-7_17 (Springer International Publishing, 2020).10.1007/978-3-030-40245-7_17 ↗
27. Atwi, R., Bliss, M., Makeev, M. & Rajput, N. N. MISPR: an open-source package for high-throughput multiscale molecular simulations. Sci. Rep. 12, 15760 (2022).10.1038/s41598-022-20009-w ↗
28. Janssen, J. et al. pyiron: an integrated development environment for computational materials science. Comput. Mater. Sci. 163, 24–36 (2019).10.1016/j.commatsci.2018.07.043 ↗
29. Kirklin, S. et al. The open quantum materials database (oqmd): assessing the accuracy of dft formation energies. npj Comput. Mater. 1, 1–15 (2015).10.1038/npjcompumats.2015.10 ↗
30. Wilkinson, M. D. et al. The FAIR guiding principles for scientific data management and stewardship. Sci. Data 3, 1–9 (2016).10.1038/sdata.2016.18 ↗
31. Huber, S. P. et al. Common workflows for computing material properties using different quantum engines. npj Comput. Mater. 7, 136 (2021).10.1038/s41524-021-00594-6 ↗
32. Ganose, A. M. et al. Atomate2: Modular Workflows for Materials Science. ChemRxiv https://doi.org/10.26434/chemrxiv-2025-tcr5h (2025).10.26434/chemrxiv-2025-tcr5h ↗
33. Rosen, A. quacc - The Quantum Accelerator https://doi.org/10.5281/zenodo.15641940 (2025).10.5281/zenodo.15641940 ↗
34. Baerends, E. J. et al. The amsterdam modeling suite. J. Chem. Phys. 162, 162501 (2025).10.1063/5.0258496 ↗
35. Marchesin, F. et al. Atomistic simulation advanced platform (asap) for materials modelling with ab initio methods. In Psi-k Conference 2022 Abstract Book (ASAP, 2022).
36. mat3ra. https://www.mat3ra.com/ (MAT3RA, 2025).
37. Meunier, M. & Robertson, S. Materials studio 20th anniversary (Materials Studio, 2021).10.1080/08927022.2021.1892093 ↗
38. Materys. https://www.materys.com (Materys, 2025).
39. Medea. https://www.materialsdesign.com/medea-software (Medea, 2025).
40. Smidstrup, S. et al. Quantumatk: an integrated platform of electronic and atomic-scale modelling tools. J. Phys. Condens. Matter 32, 015901 (2019).10.1088/1361-648X/ab4007 ↗
41. Schrödinger. https://www.schrodinger.com (Schrodinger, 2025).
42. Rêgo, C. R. et al. SimStack: an intuitive workflow framework. Front. Mater. 9, 877597 (2022).10.3389/fmats.2022.877597 ↗
43. Kokalj, A., Sbraccia, C. & Giannozzi, P. PWgui. http://www-k3.ijs.si/kokalj/pwgui/pwgui.html (PWGUI, 2014).
44. Burai. https://github.com/BURAI-team/burai (2025).
45. Mortensen, J. J. et al. GPAW: an open Python package for electronic structure calculations. J. Chem. Phys. 160, 092503 (2024).10.1063/5.0182685 ↗
46. Gupta, K., Bhattacharjee, S. & Lee, S.-C. CINEMAS: comprehensively integrated environment for advanced materials simulations. Comput. Mater. Sci. 188, 110238 (2021).10.1016/j.commatsci.2020.110238 ↗
47. Wang, G. et al. ALKEMIE: an intelligent computational platform for accelerating materials discovery and design. Comput. Mater. Sci. 186, 110064 (2021).10.1016/j.commatsci.2020.110064 ↗
48. Yakutovich, A. V. et al. AiiDAlab - an ecosystem for developing, executing, and sharing scientific workflows. Comput. Mater. Sci. 188, 110165 (2021).10.1016/j.commatsci.2020.110165 ↗
49. Giannozzi, P. et al. Quantum ESPRESSO: a modular and open-source software project for quantum simulations of materials. J. Phys. Condens. Matter 21, 395502 (2009).10.1088/0953-8984/21/39/395502 ↗
50. Giannozzi, P. et al. Advanced capabilities for materials modelling with Quantum ESPRESSO. J. Phys. Condens. Matter 29, 465901 (2017).10.1088/1361-648X/aa8f79 ↗
51. Giannozzi, P. et al. Quantum ESPRESSO toward the exascale. J. Chem. Phys. 152, 154105 (2020).10.1063/5.0005082 ↗
52. Appmode: a Jupyter extension that turns notebooks into web applications. https://github.com/oschuett/appmode (Oschuett, 2025).
53. Goel, A.Computer fundamentals (Pearson Education India, 2010).
54. Crusoe, M. R. et al. Methods included: standardizing computational reuse and portability with the common workflow language. Commun. ACM 65, 54–63 (2022).10.1145/3486897 ↗
55. Griem, L. et al. KadiStudio: FAIR modelling of scientific research processes. Data Sci. J. 21, 16–16 (2022).10.5334/dsj-2022-016 ↗
56. Andersen, C. W. et al. OPTIMADE, an API for exchanging materials data. Sci. Data 8, 217 (2021).10.1038/s41597-021-00974-z ↗
57. Evans, M. L. et al. Developments and applications of the optimade api for materials discovery, design, and data exchange. Digit. Discov. 3, 1509–1533 (2024).10.1039/D4DD00039K ↗
58. Talirz, L. et al. Materials cloud, a platform for open computational science. Sci. Data 7, 299 (2020).10.1038/s41597-020-00637-5 ↗
59. Schmidt, J., Pettersson, L., Verdozzi, C., Botti, S. & Marques, M. A. L. Crystal graph attention networks for the prediction of stable materials. Sci. Adv. 7, eabi7948 (2021).10.1126/sciadv.abi7948 ↗
60. Schmidt, J. et al. Machine-learning-assisted determination of the global zero-temperature phase diagram of materials. Adv. Mater. 35, 2210788 (2023).10.1002/adma.202210788 ↗
61. Mosquera-Lois, I., Kavanagh, S. R., Walsh, A. & Scanlon, D. O. ShakeNBreak: navigating the defect configurational landscape. J. Open Source Softw. 7, 4817 (2022).10.21105/joss.04817 ↗
62. de Miranda Nascimento, G. et al. Accurate and efficient protocols for high-throughput first-principles materials simulations. arXiv 2504.03962 (2025).
63. Prandini, G., Marrazzo, A., Castelli, I. E., Mounet, N. & Marzari, N. Precision and efficiency in solid-state pseudopotential calculations. npj Comput. Mater. 4, 72 (2018).10.1038/s41524-018-0127-2 ↗
64. van Setten, M. et al. The PseudoDojo: training and grading a 85 element optimized norm-conserving pseudopotential table. Computer Phys. Commun. 226, 39–54 (2018).10.1016/j.cpc.2018.01.012 ↗
65. Grosso, G. & Parravicini, G. P. Solid State Physics, second edn, https://www.sciencedirect.com/science/article/pii/B9780123850300000050 (Academic Press, 2014).
66. Hinuma, Y., Pizzi, G., Kumagai, Y., Oba, F. & Tanaka, I. Band structure diagram paths based on crystallography. Comput. Mater. Sci. 128, 140–184 (2017).10.1016/j.commatsci.2016.10.015 ↗
67. Togo, A., Shinohara, K. & Tanaka, I. Spglib: a software library for crystal symmetry search. Sci. Technol. Adv. Mater. Methods 4, 2384822 (2024).10.1080/27660400.2024.2384822 ↗
68. Schuler, B. et al. How substitutional point defects in two-dimensional WS2 induce charge localization, spin-orbit splitting, and strain. ACS Nano 13, 10520–10534 (2019).10.1021/acsnano.9b04611 ↗
69. Nielsen, R. S. et al. BaZrS3 lights up: The interplay of electrons, photons, and phonons in strongly luminescent single crystals. Adv. Opt. Mater. 13, e00915 (2025).10.1002/adom.202500915 ↗
70. Bastonero, L. & Marzari, N. Automated all-functionals infrared and Raman spectra. npj Comput. Mater. 10, 55 (2024).10.1038/s41524-024-01236-3 ↗
71. Bastonero, L. aiida-phonopy. https://github.com/aiida-phonopy/aiida-phonopy (2025).
72. Togo, A., Chaput, L., Tadano, T. & Tanaka, I. Implementation strategies in phonopy and phono3py. J. Phys. Condens. Matter 35, 353001 (2023).10.1088/1361-648X/acd831 ↗
73. Togo, A. First-principles phonon calculations with phonopy and phono3py. J. Phys. Soc. Jpn. 92, 012001 (2023).10.7566/JPSJ.92.012001 ↗
74. Baroni, S., de Gironcoli, S., Dal Corso, A. & Giannozzi, P. Phonons and related crystal properties from density-functional perturbation theory. Rev. Mod. Phys. 73, 515–562 (2001).10.1103/RevModPhys.73.515 ↗
75. Umari, P. & Pasquarello, A. Ab initio Molecular Dynamics in a Finite Homogeneous Electric Field. Phys. Rev. Lett. 89, 157602 (2002).10.1103/PhysRevLett.89.157602 ↗
76. Fair, R. et al. Euphonic: inelastic neutron scattering simulations from force constants and visualization tools for phonon properties. J. Appl. Crystallogr. 55, 1689–1703 (2022).10.1107/S1600576722009256 ↗
77. van Bokhoven, J. A. & Lamberti, C.X-Ray Absorption and X-Ray Emission Spectroscopy, vol. 1-2 https://doi.org/10.1002/9781118844243 (Wiley, 2016).10.1002/9781118844243 ↗
78. Gougoussis, C., Calandra, M., Seitsonen, A. P. & Mauri, F. First-principles calculations of x-ray absorption in a scheme based on ultrasoft pseudopotentials: From α-quartz to high-Tc compounds. Phys. Rev. B 80, 075102 (2009).10.1103/PhysRevB.80.075102 ↗
79. Bunău, O. & Calandra, M. Projector augmented wave calculation of x-ray absorption spectra at the L2,3 edges. Phys. Rev. B 87, 205105 (2013).10.1103/PhysRevB.87.205105 ↗
80. Triguero, L., Pettersson, L. & Ågren, H. Calculations of near-edge x-ray-absorption spectra of gas-phase and chemisorbed molecules by means of density-functional and transition-potential theory. Phys. Rev. B 58, 8097 (1998).10.1103/PhysRevB.58.8097 ↗
81. Cavigliasso, G. & Chong, D. P. Accurate density-functional calculation of core-electron binding energies by a total-energy difference approach. J. Chem. Phys. 111, 9485–9492 (1999).10.1063/1.480279 ↗
82. Walter, M., Moseler, M. & Pastewka, L. Offset-corrected δ-Kohn-Sham scheme for semiempirical prediction of absolute X-ray photoelectron energies in molecules and solids. Phys. Rev. B 94, 041112 (2016).10.1103/PhysRevB.94.041112 ↗
83. de Réotier, P. D. & Yaouanc, A. Muon spin rotation and relaxation in magnetic materials. J. Phys. Condens. Matter 9, 9113 (1997).10.1088/0953-8984/9/43/002 ↗
84. Möller, J. S., Ceresoli, D., Lancaster, T., Marzari, N. & Blundell, S. J. Quantum states of muons in fluorides. Phys. Rev. B 87, 121108 (2013).10.1103/PhysRevB.87.121108 ↗
85. Bernardini, F., Bonfà, P., Massidda, S. & De Renzi, R. Ab initio strategy for muon site assignment in wide band gap fluorides. Phys. Rev. B 87, 115148 (2013).10.1103/PhysRevB.87.115148 ↗
86. Blundell, S. J. et al. Electronic structure calculations for muon spectroscopy. Electron. Struct. 7, 023001 (2025).10.1088/2516-1075/adcb7c ↗
87. Onuorah, I. J. et al. Automated computational workflows for muon spin spectroscopy. Digital Discov. 4, 523–538 (2025).10.1039/D4DD00314D ↗
88. Onuorah, I. J. et al. Positivemuon/aiida-muon: v1.0.3, https://doi.org/10.5281/zenodo.14594493 (2025).10.5281/zenodo.14594493 ↗
89. Bonfà, P., Frassineti, J., Isah, M. M., Onuorah, I. J. & Sanna, S. UNDI: an open-source library to simulate muon-nuclear interactions in solids. Computer Phys. Commun. 260, 107719 (2021).10.1016/j.cpc.2020.107719 ↗
90. Marzari, N. & Vanderbilt, D. Maximally localized generalized Wannier functions for composite energy bands. Phys. Rev. B 56, 12847 (1997).10.1103/PhysRevB.56.12847 ↗
91. Marzari, N., Mostofi, A. A., Yates, J. R., Souza, I. & Vanderbilt, D. Maximally localized Wannier functions: theory and applications. Rev. Mod. Phys. 84, 1419–1475 (2012).10.1103/RevModPhys.84.1419 ↗
92. Marrazzo, A. et al. Wannier-function software ecosystem for materials simulations. Rev. Mod. Phys. 96, 045008 (2024).10.1103/RevModPhys.96.045008 ↗
93. Damle, A., Lin, L. & Ying, L. Compressed representation of Kohn–Sham orbitals via selected columns of the density matrix. J. Chem. Theory Comput. 11, 1463–1469 (2015).10.1021/ct500985f ↗
94. Vitale, V. et al. Automated high-throughput Wannierisation. npj Comput. Mater. 6, 66 (2020).10.1038/s41524-020-0312-y ↗
95. Qiao, J., Pizzi, G. & Marzari, N. Projectability disentanglement for accurate and automated electronic-structure Hamiltonians. npj Comput. Mater. 9, 208 (2023).10.1038/s41524-023-01146-w ↗
96. Biberacher, W. Shubnikov-de Haas and de Haas-van Alphen Techniques. Encycl. Condens. Matter Phys. 4, 228–236 (2005).10.1016/B978-0-323-90800-9.00300-0 ↗
97. Bergemann, C. Fermi Surface Measurements. Encyclopedia of Condensed Matter Physics 185–192 (Elsevier, 2005).10.1016/B0-12-369401-9/00456-3 ↗
98. Anisimov, V., Zaanen, J. & Andersen, O. Band theory and Mott insulators: Hubbard U instead of Stoner I. Phys. Rev. B 44, 943 (1991).10.1103/PhysRevB.44.943 ↗
99. Liechtenstein, A., Anisimov, V. & Zaanen, J. Density-functional theory and strong interactions: orbital ordering in Mott-Hubbard insulators. Phys. Rev. B 52, R5467 (1995).10.1103/PhysRevB.52.R5467 ↗
100. Dudarev, S., Botton, G., Savrasov, S., Humphreys, C. & Sutton, A. Electron-energy-loss spectra and the structural stability of nickel oxide: an LSDA+U study. Phys. Rev. B 57, 1505 (1998).10.1103/PhysRevB.57.1505 ↗