Welcome to the documentation of the aiida-yambo package!#
AiiDA Yambo is a package that allows to automate many-body perturbation theory calculations using the yambo-code in the AiiDA framework.
Currently, the aiida-yambo plugin supports
quasiparticle (G0W0, COSHEX and HFlevel)
and optical properties (IP-RPA and BSE) simulations,
as well as interfaces with different codes (e.g., Quantum ESPRESSO and Wannier90).
Instructions to install, configure and setup the plugin package.
Consult the user guide and tutorials.
How to cite#
If you use this package for your research, please cite the following works:
M. Bonacci, J. Qiao , N. Spallanzani, A. Marrazzo, G. Pizzi, E. Molinari, D. Varsano, A. Ferretti, D. Prezzi, Towards high-throughput many-body perturbation theory: efficient algorithms and automated workflows, npj Computational Materials, 9, 74 (2023).
D. Sangalli, A. Ferretti, H. Miranda, C. Attaccalite, I. Marri, E. Cannuccia, P. Melo, M. Marsili, F. Paleari, A. Marrazzo, G. Prandini, P. Bonfà, M.O. Atambo, F. Affinito, M. Palummo, A. Molina-Sánchez, C. Hogan, M. Grüning, D. Varsano, A. Marini, Many-body perturbation theory calculations using the yambo code, J. Phys. Condens. Matter, 31, 325902 (2019).
S. P. Huber, S. Zoupanos, M. Uhrin, L. Talirz, L. Kahle, R. H ̈auselmann, D. Gresch, T. M ̈uller, A. V. Yakutovich, C. W. Andersen, F. F. Ramirez, C. S. Adorf, F. Gargiulo, S. Kumbhar, E. Passaro, C. Johnston, A. Merkys, A. Cepellotti, N. Mounet, N. Marzari, B. Kozinsky, and G. Pizzi, AiiDA 1.0, a scalable computational infrastructure for automated reproducible workflows and data provenance, Sci. Data 7, 300 (2020).
Martin Uhrin, Sebastiaan. P. Huber, Jusong Yu, Nicola Marzari, and Giovanni Pizzi, Workflows in AiiDA: Engineering a high-throughput, event-based engine for robust and modular computational workflows, Computational Materials Science 187, 110086 (2021)
G. Pizzi, A. Cepellotti, R. Sabatini, N. Marzari, and B. Kozinsky, AiiDA: automated interactive infrastructure and database for computational science, Comp. Mat. Sci 111, 218-230 (2016).
Acknoledgements#
This work was supported by: the Centre of Excellence “MaX - Materials Design at the Exascale” funded by European Union (H2020-EINFRA-2015-1, Grant No. 676598; H2020-INFRAEDI-2018-1, Grant No. 824143; HORIZON-EUROHPC-JU-2021-COE-1 , Grant No. 101093374); the European Union’s Horizon 2020 research and innovation programme (BIG-MAP, Grant No. 957189, also part of the BATTERY 2030+ initiative, Grant No. 957213); NCCR MARVEL, a National Centre of Competence in Research, funded by the Swiss National Science Foundation (Grant No. 205602).
