AiiDA pseudo plugin#
The aiida-pseudo
package is an AiiDA plugin that simplifies working with pseudopotentials when running calculations or workflows using AiiDA.
Installation#
You can install aiida-pseudo
in your Python environment using pip
:
$ pip install aiida-pseudo
Getting Started#
The easiest way of getting started using aiida-pseudo
is to use the command line interface that ships with it.
For example, to install a configuration of the SSSP, just run:
$ aiida-pseudo install sssp
The version, functional, and protocol can be controlled with various options; use aiida-pseudo install sssp --help
to see their description.
If you are experiencing problems with this automated install method, see the Troubleshooting section for help.
Installed pseudopotential families can be listed using:
$ aiida-pseudo list
Any pseudopotential family installed can be loaded like any other Group
using the load_group
utility from aiida-core
.
Once loaded, it is easy to get the pseudopotentials for a given element or set of elements.
Open a verdi shell
and run:
family = load_group('SSSP/1.1/PBE/efficiency')
pseudo = family.get_pseudo(element='Ga') # Returns a single pseudo
pseudos = family.get_pseudos(elements=('Ga', 'As')) # Returns a dictionary of pseudos where the keys are the elements
If you have a StructureData
node, the get_pseudos
method also accepts that as an argument to automatically retrieve all the pseudopotentials required for that structure:
structure = load_node(<IDENTIFIER>) # Load the structure from database or create one
pseudos = family.get_pseudos(structure=structure)
If you use the aiida-quantumespresso
plugin, the pseudos
dictionary returned by get_pseudos
can be directly used as an input for a PwCalculation
.
Contents#
Acknowledgements#
If you use this plugin and/or AiiDA for your research, please cite the following work:
Sebastiaan. P. Huber, Spyros Zoupanos, Martin Uhrin, Leopold Talirz, Leonid Kahle, Rico Häuselmann, Dominik Gresch, Tiziano Müller, Aliaksandr V. Yakutovich, Casper W. Andersen, Francisco F. Ramirez, Carl S. Adorf, Fernando Gargiulo, Snehal Kumbhar, Elsa Passaro, Conrad Johnston, Andrius Merkys, Andrea Cepellotti, Nicolas Mounet, Nicola Marzari, Boris Kozinsky, and Giovanni Pizzi, AiiDA 1.0, a scalable computational infrastructure for automated reproducible workflows and data provenance, Scientific 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)
We acknowledge support from:
The NCCR MARVEL funded by the Swiss National Science Foundation. |
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The EU Centre of Excellence “MaX – Materials Design at the Exascale” (Horizon 2020 EINFRA-5, Grant No. 676598). |
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