3. Your first workchain

Here we will continue where the previous tutorial left of. In particular we will now create a dedicated workchain that calls the VASP workchain and thus eventually executes VASP for each volume. The workchain makes sure the total energies are extracted and stored. In the end we will even use some Python functions to locate the minimum and store that as well. We hope that you after this tutorial will finally start to appreciate the convenience of AiiDA-VASP and maybe also start to get a feel for what is possible. Even better, when performing calculations like this, especially when the workchain stack becomes much more complex than showed in this example, the workflow becomes standardized and versioned, so it is easier to share and collaborate. Also, reproducibility is finally possible in practice. Let us start to develop the workchain. As before we assume you have completed the steps of the previous tutorial.

In order to make it as easy as possible, we have created a cookie cutter recipe for creating AiiDA-VASP workchains. This is located in cookiecutters/workchain on the root of the repository. If you have not cloned it, you can download only the cookiecutter folder using:

$ svn checkout https://github.com/aiida-vasp/aiida-vasp/trunk/cookiecutters

What we want to do is to create a workchain that calculates some equation of state (EOS), in this case over volume. For simplicity we will call the workchain EosWorkChain. It is always recommended to dissect the operations we will perform into parts before writing the workchain. In this way, it is easier to reuse larger parts of the workchain at a later point. Also, it is easier to understand what is going on from just inspecting the definitions of the inputs, steps and outputs of the workchain. Please, have a look at Workchains and references therein before continuing. Also, consider to look into the tutorial on writing workflows in the AiiDA documentation before continuing.

  1. First, let us Make sure you have cookiecutter installed:

    $ pip install cookiecutter
  2. Now we simply execute the cookiecutter program using the workchains folder as the template:

    $ cookiecutter cookiecutters/workchains
    workchain_name [testchain]: eos
    workchain_docstring [This workchain is a testchain]: This workchain will accept a dictionary of structures and extract the total energies for each structure. The data is saved and the energy minimum is calculated and stored.
    next_workchain_to_call [vasp.vasp]:
  3. A new folder eos was created which contains the eos.py workchain file. Inspect it. Maybe the most important part of a workchain is the define function, which, as its name implies, defines the workchain. For this particular case it looks like:

        def define(cls, spec):
            super(EosWorkChain, cls).define(spec)
            spec.expose_inputs(cls._next_workchain, exclude=['structure'])
                'structures', valid_type=DataFactory('structure'), dynamic=True, help='a dictionary of structures to use'
            spec.exit_code(0, 'NO_ERROR', message='the sun is shining')
            spec.exit_code(420, 'ERROR_NO_CALLED_WORKCHAIN', message='no called workchain detected')
            spec.exit_code(500, 'ERROR_UNKNOWN', message='unknown error detected in the eos workchain')
            )  # yapf: disable
            spec.output('quantities', valid_type=DataFactory('array'), help='a container for various quantities')

    We have defined some inputs:

    • All inputs defined in the _next_workchain, which in this case is the VASP workchain are also available for this workchain by using expose_inputs. You can of course expose inputs from other workchains as long as AiiDA can find them. In this way, we do not have to repeat the definition of more basic workchains that can easily be repeated, like the VASP workchain.

    • A namespace called structures, which is defined as a dictionary. Since we here do not know how many structures will be supplied, we have defined this input as a dynamic namespace. However, we know the dictionary should only contain StructureData values.

    Then we have defined some outputs:

    • We attach all outputs from _next_workchain, i.e. the VASP workchain to this workchain (basically a link is created to avoid storing things twice). Similar to the inputs, expose_outputs can be leveraged.

    And some generic exit codes.

    Finally, there is the outline section which tells the workchain which functions to run. In this case we have:

    • A function that initializes what we need, initialize.

    • A while loop that iterates over all the structures.

    • A init_next_workchain function that initializes what we need for the next workchain at the current iteration (here we make sure the specific structure is set).

    • A run_next_workchain which basically executes VASP workchain.

    • A verify_next_workchain which verifies that there is a valid VASP workchain and inherits any exit codes present from VASP workchain.

    • A extract_results which gets what we need from the outputs.

    • A finalize which stores the results.

    Notice that the cookiecutter gave all this automatically, which is rather useful as a starting point to develop new workchains.

  4. The workchain is however not yet ready. We need to extract the total energies and specify the general workchain further. Please make the following changes to the generated workchain:

    --- /home/docs/checkouts/readthedocs.org/user_builds/aiida-vasp/checkouts/latest/tutorials/eos_base.py
    +++ /home/docs/checkouts/readthedocs.org/user_builds/aiida-vasp/checkouts/latest/tutorials/eos.py
    @@ -11,6 +11,8 @@
     import random
     import numpy as np
    +from scipy.interpolate import interp1d
    +from scipy.optimize import minimize
     from aiida.common.extendeddicts import AttributeDict
     from aiida.engine import WorkChain, append_, calcfunction, while_
    @@ -51,13 +53,19 @@
    -                cls.extract_quantities
    +                cls.extract_volume_and_energy
             )  # yapf: disable
    -        spec.output('quantities', valid_type=DataFactory('array'), help='a container for various quantities')
    -        spec.expose_outputs(cls._next_workchain)
    +        spec.output(
    +            'eos', valid_type=DataFactory('array'), help='a list containing the cell volumes and total energies'
    +        )
    +        spec.output(
    +            'eos_minimum',
    +            valid_type=DataFactory('dict'),
    +            help='a dictionary containing the cell volume at energy minimum'
    +        )
         def initialize(self):
             """Initialize the eos workchain."""
    @@ -82,8 +90,8 @@
             # Define the context inputs
             self.ctx.inputs = AttributeDict()
    -        # Define container to store quantities that is extracted in each step
    -        self.ctx.quantities_container = []
    +        # Define the total energies list
    +        self.ctx.total_energies = []
         def _init_inputs(self):
             """Initialize inputs."""
    @@ -125,7 +133,7 @@
             self.ctx.inputs.structure = self.ctx.structures.pop(item)
             # Make sure we do not have any floating dict (convert to Dict etc.)
    -        self.ctx.inputs = prepare_process_inputs(self.ctx.inputs)
    +        self.ctx.inputs = prepare_process_inputs(self.ctx.inputs, namespaces=['dynamics'])
         def run_next_workchain(self):
    @@ -170,35 +178,74 @@
             return self.ctx.exit_code
    -    def extract_quantities(self):
    -        """Extract the quantities you want and store them in the container."""
    -        # What happens in this routine needs modifications by you.
    -        # Typically you get the output of the called workchain by doing
    -        # workchain = self.ctx.workchains[-1]
    -        # some_output_quantity = workchain.outputs.some_output_quantity
    -        # An example which stores nonsense.
    -        self.ctx.quantities_container.append([self.ctx.iteration, f'Some quantity for iteration: {self.ctx.iteration}'])
    +    def extract_volume_and_energy(self):
    +        """Extract the cell volume and total energy for this structure."""
    +        workchain = self.ctx.workchains[-1]
    +        # Fetch the total energy
    +        misc = workchain.outputs.misc.get_dict()
    +        total_energy = misc['total_energies']['energy_extrapolated']
    +        # Fetch the volume
    +        volume = self.ctx.inputs.structure.get_cell_volume()
    +        # Store both in a list
    +        self.ctx.total_energies.append([volume, total_energy])
         def finalize(self):
             Finalize the workchain.
    -        Take the quantity container and set it as an output of this workchain.
    +        Take the total energies container and set is as an output of this workchain.
             # Due to data provenance we cannot return AiiDA data containers that have
             # not been passed through a calcfunction, workfunction or a workchain. Create this now.
    -        quantities_container = store_quantities(DataFactory('list')(list=self.ctx.quantities_container))
    +        total_energies = store_total_energies(DataFactory('list')(list=self.ctx.total_energies))
    +        # Let us also try to find a better minimum, just as an example using the power of Python
    +        energy = locate_minimum(total_energies)
             # And then store the output on the workchain
    -        self.out('quantities', quantities_container)
    +        self.out('eos', total_energies)
    +        self.out('eos_minimum', energy)
    -def store_quantities(quantities_container):
    -    """Stores the quantities to keep data provenance."""
    -    quantities_container_list = quantities_container.get_list()
    -    quantities_container_array = DataFactory('array')()
    -    quantities_container_array.set_array('quantities', np.array(quantities_container_list))
    -    return quantities_container_array
    +def store_total_energies(total_energies):
    +    """Stores the total energies in ArrayData to keep data provenance."""
    +    total_energies_list = total_energies.get_list()
    +    # Let us also sort by volume as we picked the entries in the structures by random
    +    # above
    +    total_energies_array = np.array(total_energies_list)
    +    total_energies_array_sorted = total_energies_array[total_energies_array[:, 0].argsort()]
    +    array_data = DataFactory('array')()
    +    array_data.set_array('eos', total_energies_array_sorted)
    +    return array_data
    +def locate_minimum(total_energies):
    +    """Locate the volume with the lowest energy using interpolation."""
    +    total_energies_array = total_energies.get_array('eos')
    +    volumes = total_energies_array[:, 0]
    +    energies = total_energies_array[:, 1]
    +    # Establish some initial guess (take lowest energy point from the original dataset)
    +    min_energy_guess_index = np.argmin(energies)
    +    # Create the function that can be used to extract interpolated values
    +    # Using cubic interpolation here, which is not necessarly physically correct,
    +    # only serves as an example. Please have a look at the theory behind more realiastic
    +    # models that can be used to fit such data.
    +    new_energies = interp1d(volumes, energies, kind='cubic')
    +    # Use minimize from scipy to locate the minimal point that can be ejected by the
    +    # interpolation routines
    +    minimized_point = minimize(new_energies, volumes[min_energy_guess_index], tol=1e-3)
    +    # Create a dictionary to house the results and return
    +    dict_data = DataFactory('dict')(dict={'volume': minimized_point.x[0], 'energy': minimized_point.fun})
    +    return dict_data

    and save it as eos.py. Or you could also download it:

    $ wget https://github.com/aiida-vasp/aiida-vasp/raw/master/tutorials/eos.py

    The majority of changes were related to being more specific, except two things:

    • The necessity of decorating the function that generates the output array containing the volume and total energies in a calcfunction. This is to preserve data provenance, otherwise we would not have known how the data was collected from each of the underlying workchains.

    • The inclusion of a calcfunction that interpolates the calculated data to find a better estimate of the volume at the energy minima. The example uses a cubic fit, which is certainly not very physical and should not be used in production. It is only to show how simply it is to leverage the power of Python, NumPy, SciPy and AiiDA. This was decorated with a calcfunction in order to make sure AiiDA can honor data provenance.

  5. Next, download the launch script that is tailored to launch the workchain we have now developed:

    $ wget https://github.com/aiida-vasp/aiida-vasp/raw/master/tutorials/run_fcc_si_workchain.py
  6. Change the options and code_string as you did in previously.

  7. Now we need to make sure the daemon can pick up the workchain. We can do this by making sure the daemon sees the directory where eos.py and run_fcc_si_workchain.py is located. The simplest approach is to add the following, to your virtual environment activate script (assuming you do not use Conda):

    $ echo "export PYTHONPATH=$PYTHONPATH:<yourdirectory>" >> ~/env/aiida-vasp/bin/activate

    assuming <yourdirectory> is the directory containing the eos.py and run_fcc_si_workchain.py files. The location of the activate is assumed from the previous steps in the tutorial. If you use Conda, please do:

    $ echo "export PYTHONPATH=$PYTHONPATH:<yourdirectory>" >> $CONDA_PREFIX/etc/conda/activate.d/env_vars.sh


    Make sure you have (re)activated your AiiDA virtual environment and (re)started the AiiDA daemon before continuing. Otherwise, the updated PYTHONPATH variable will not be picked up by the daemon worker. You may want to double check by running echo $PYTHONPATH in the shell before starting the daemon with verdi daemon start.

  8. Submit the workchain by running the call script:

    $ python run_fcc_si_workchain.py
  9. After a while we check the status:

    $ verdi process list -a
      PK  Created    Process label         Process State      Process status
    ----  ---------  --------------------  -----------------  -----------------------------------------------------------
    1721  22m ago    EosWorkChain          ⏹ Finished [0]
    1722  22m ago    VaspWorkChain         ⏹ Finished [0]
    1724  22m ago    VaspCalculation       ⏹ Finished [0]
    1728  19m ago    VaspWorkChain         ⏹ Finished [0]
    1730  19m ago    VaspCalculation       ⏹ Finished [0]
    1734  17m ago    VaspWorkChain         ⏹ Finished [0]
    1736  17m ago    VaspCalculation       ⏹ Finished [0]
    1740  15m ago    VaspWorkChain         ⏹ Finished [0]
    1742  15m ago    VaspCalculation       ⏹ Finished [0]
    1746  13m ago    VaspWorkChain         ⏹ Finished [0]
    1748  13m ago    VaspCalculation       ⏹ Finished [0]
    1752  10m ago    VaspWorkChain         ⏹ Finished [0]
    1754  10m ago    VaspCalculation       ⏹ Finished [0]
    1758  8m ago     VaspWorkChain         ⏹ Finished [0]
    1760  8m ago     VaspCalculation       ⏹ Finished [0]
    1764  6m ago     VaspWorkChain         ⏹ Finished [0]
    1766  6m ago     VaspCalculation       ⏹ Finished [0]
    1770  4m ago     VaspWorkChain         ⏹ Finished [0]
    1772  3m ago     VaspCalculation       ⏹ Finished [0]
    1777  1m ago     store_total_energies  ⏹ Finished [0]
    1779  1m ago     locate_minimum        ⏹ Finished [0]
    Total results: 165
    Report: last time an entry changed state: 1m ago (at 17:18:23 on 2022-12-21)
    Report: Using 2% of the available daemon worker slots.

    As you can see, seven VASP workchain and VASP calculation were executed, one for each supplied volume. Also, there is a separate entry for the storage of the total energies, which also performs a sort. The location of the minima is also listed as a separate process as we decorated that function with a calcfunction decorator.

  10. Let us have a look at the output of EosWorkChain:

    $ verdi process show 1721
    Property     Value
    -----------  ------------------------------------
    type         EosWorkChain
    state        Finished [0]
    pk           1721
    uuid         69e9a920-c783-4f61-ac73-349b6e19059d
    ctime        2022-12-21 16:58:07.145071+01:00
    mtime        2022-12-21 17:18:23.379756+01:00
    Inputs              PK    Type
    ------------------  ----  -------------
        silicon_at_3_5  1703  StructureData
        silicon_at_3_6  1704  StructureData
        silicon_at_3_7  1705  StructureData
        silicon_at_3_8  1706  StructureData
        silicon_at_3_9  1707  StructureData
        silicon_at_4_0  1708  StructureData
        silicon_at_4_1  1709  StructureData
        silicon_at_4_2  1710  StructureData
        silicon_at_4_3  1711  StructureData
    clean_workdir       1720  Bool
    code                818   InstalledCode
    kpoints             1712  KpointsData
    max_iterations      1719  Int
    options             1716  Dict
    parameters          1713  Dict
    potential_family    1714  Str
    potential_mapping   1715  Dict
    settings            1717  Dict
    verbose             1718  Bool
    Outputs        PK  Type
    -----------  ----  ---------
    eos          1778  ArrayData
    eos_minimum  1780  Dict
    Called      PK  Type
    --------  ----  --------------------
    CALL      1722  VaspWorkChain
    CALL      1728  VaspWorkChain
    CALL      1734  VaspWorkChain
    CALL      1740  VaspWorkChain
    CALL      1746  VaspWorkChain
    CALL      1752  VaspWorkChain
    CALL      1758  VaspWorkChain
    CALL      1764  VaspWorkChain
    CALL      1770  VaspWorkChain
    CALL      1777  store_total_energies
    CALL      1779  locate_minimum
    Log messages
    There are 9 log messages for this calculation
    Run 'verdi process report 1721' to see them
  11. Inspect the total energies versus volume:

     $ verdi data core.array show 1777
         "eos": [
  12. And the located minimum:

    $ verdi data core.dict show 1780
        "energy": -4.8769540208841,
        "volume": 14.559367617229

That concludes this tutorial. We hope at this point you have now realized that AiiDA-VASP seems somewhat useful and that you would like to continue to learn more, maybe even start to write your own Workflows or Workchains. You might have noticed when running this workflow that the each volume was running sequentially and was a bit concerned about that being not so efficient as there is no data sharing between the different volume runs. And indeed you are right. The next tutorial will show how this can be addressed.