Package: aiida-quantumespresso
aiida-quantumespressoThis is the official AiiDA plugin forQuantum ESPRESSO.Compatibility matrixThe matrix below assumes the user always install the latest patch release of the specified minor version, which is recommended.PluginAiiDAPythonQuantum ESPRESSOv4.3 < v5.0v4.0 < v4.3v3.5 < v4.0v3.4 < v3.5v3.3 < v3.4v3.1 < v3.3v3.0 < v3.1v2.0 < v3.0Starting fromaiida-quantumespresso==4.0, the last three minor versions of Quantum ESPRESSO are supported.
Older versions are supported up to a maximum of two years.InstallationTo install from PyPI, simply execute:pip install aiida-quantumespressoor when installing from source:git clone https://github.com/aiidateam/aiida-quantumespresso
pip install aiida-quantumespressoCommand line interface toolThe plugin comes with a builtin CLI tool:aiida-quantumespresso.
This tool is built using theclicklibrary and supports tab-completion.
To enable it, add the following to your shell loading script, e.g. the.bashrcor virtual environment activate script:eval "$(_AIIDA_QUANTUMESPRESSO_COMPLETE=source aiida-quantumespresso)"The tool comes with various sub commands, for example to quickly launch some calculations and workchains
For example, to launch a testPwCalculationyou can run the following command:aiida-quantumespresso calculation launch pw -X pw-v6.1 -F SSSP/1.1/PBE/efficiencyNote that this requires the codepw-v6.1and pseudopotential familySSSP/1.1/PBE/efficiencyto be configured.
See the pseudopotentials section on how to install them easily.
Each command has a fully documented command line interface, which can be printed to screen with the help flag:aiida-quantumespresso calculation launch ph --helpwhich should print something like the following:Usage: aiida-quantumespresso calculation launch ph [OPTIONS]

  Run a PhCalculation.

Options:
  -X, --code CODE                 A single code identified by its ID, UUID or
                                  label.  [required]
  -C, --calculation CALCULATION   A single calculation identified by its ID or
                                  UUID.  [required]
  -k, --kpoints-mesh INTEGER...   The number of points in the kpoint mesh
                                  along each basis vector.  [default: 1, 1, 1]
  -m, --max-num-machines INTEGER  The maximum number of machines (nodes) to
                                  use for the calculations.  [default: 1]
  -w, --max-wallclock-seconds INTEGER
                                  the maximum wallclock time in seconds to set
                                  for the calculations.  [default: 1800]
  -i, --with-mpi                  Run the calculations with MPI enabled.
                                  [default: False]
  -d, --daemon                    Submit the process to the daemon instead of
                                  running it locally.  [default: False]
  -h, --help                      Show this message and exit.PseudopotentialsPseudopotentials are installed and managed through theaiida-pseudoplugin.
The easiest way to install pseudopotentials, is to install a version of theSSSPthrough the CLI ofaiida-pseudo.
Simply runaiida-pseudo install ssspto install the default SSSP version.
List the installed pseudopotential families with the commandaiida-pseudo list.
You can then use the name of any family in the command line using the-Fflag.DevelopmentRunning testsTo run the tests, simply clone and install the package locally with the [tests] optional dependencies:gitclonehttps://github.com/aiidateam/aiida-quantumespresso.cdaiida-quantumespresso
pipinstall-e.[tests]# install extra dependencies for testpytest# run testsYou can also usetoxto run the test set. Here you can also use the-eoption to specify the Python version for the test run:pipinstalltox
tox-epy39--tests/calculations/test_pw.pyPre-commitTo contribute to this repository, please enable pre-commit so the code in commits are conform to the standards.
Simply install the repository with thepre-commitextra dependencies:cdaiida-quantumespresso
pipinstall-e.[pre-commit]pre-commitinstallLicenseTheaiida-quantumespressoplugin package is released under the MIT license.
See theLICENSE.txtfile for more details.AcknowledgementsWe acknowledge support from:theNCCR MARVELfunded by the Swiss National Science Foundation;the EU Centre of Excellence "MaX – Materials Design at the Exascale" (Horizon 2020 EINFRA-5, 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 (Grant No. 957189,project BIG-MAP, also part of theBATTERY 2030+ initiative, Grant No. 957213);theswissuniversities P-5 project "Materials Cloud".