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machine learning interatomic potentials aiida plugin

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machine learning interatomic potentials aiida plugin

This plugin is the default output of the AiiDA plugin cutter, intended to help developers get started with their AiiDA plugins.

Repository contents

See also the following video sequences from the 2019-05 AiiDA tutorial:

For more information, see the developer guide of your plugin.


  • Add input files using SinglefileData:

    SinglefileData = DataFactory('core.singlefile')
    inputs['file1'] = SinglefileData(file='/path/to/file1')
    inputs['file2'] = SinglefileData(file='/path/to/file2')
  • Specify command line options via a python dictionary and DiffParameters:

    d = { 'ignore-case': True }
    DiffParameters = DataFactory('mlip')
    inputs['parameters'] = DiffParameters(dict=d)
  • DiffParameters dictionaries are validated using voluptuous. Find out about supported options:

    DiffParameters = DataFactory('mlip')


pip install aiida-mlip
verdi quicksetup  # better to set up a new profile
verdi plugin list aiida.calculations  # should now show your calclulation plugins


Here goes a complete example of how to submit a test calculation using this plugin.

A quick demo of how to submit a calculation:

verdi daemon start     # make sure the daemon is running
cd examples
./        # run test calculation
verdi process list -a  # check record of calculation

The plugin also includes verdi commands to inspect its data types:

verdi data mlip list
verdi data mlip export <PK>


  1. Install poetry
  2. (Optional) Create a virtual environment
  3. Install aiida-mlip with dependencies:
git clone
cd aiida-mlip
pip install --upgrade pip
poetry install --with pre-commit,dev,docs  # install extra dependencies
pre-commit install  # install pre-commit hooks
pytest -v  # discover and run all tests

See the developer guide for more information.


BSD 3-Clause License


Contributors to this project were funded by


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