FHI-Aims Calculator

This example illustrates the usage of Workflow for efficiently performing density-functioncal theory (DFT) calculations with the all-electron electronic structure code FHI-Aims.

At first, we initialize a ConfigSet with a couple of (periodic and non-periodic) systems stored in Input_Structures.xyz. We also define an OutputSpec to handle the output that will additionally comprise values calculated by FHI-Aims.

Next, we define various parameter that will be used during the calculations. Most of them define parameter settings for the DFT calculation (that will be written in the control.in input file of FHI-Aims). Note that for non-periodic systems the calculations will be performed with the exact same settings, but without parameter specific to periodic systems (e.g. k_grid, compute_analytical_stress, etc.). The last parameter calculator_exec defines the command used to call FHI-Aims and start an individual calculation, including specifications for the parallelization of that specific calcuation and excluding any redirection of the output (i.e. without e.g. >> aims.out).

Now we define a calculator of the form (calc_constructor, args, kwargs) using the Aims calculator of Workflow and the parameter dictionary we have just defined. Finally, we apply the generic.calculate() function to process all the input structures in parallel (defined via the autopara_info settings). The values of the calculated properties will be written into Output_Structures.xyz with keys that have the specified output_prefix. Note that for non-periodic systems the property “stress” will be ignored.

import pathlib

import wfl
from wfl.calculators.aims import Aims
from wfl.calculators import generic
from wfl.autoparallelize import AutoparaInfo
from wfl.configset import ConfigSet, OutputSpec


workdir = pathlib.Path(wfl.__file__).parents[1]/"docs/source/examples_files/fhiaims_calculator"
inputs = ConfigSet(workdir/'./Input_Structures.xyz')
outputs = OutputSpec('Output_Structures.xyz')

aims_kwargs = {
        'xc':                        'pbe',
        'spin':                      'none',
        'relativistic':              'none',
        'charge':                    0.,
        'sc_iter_limit':             500,
        'occupation_type':           'gaussian 0.01',
        'charge_mix_param':          0.6,
        'mixer':                     'pulay',
        'n_max_pulay':               10,
        'sc_accuracy_rho':           1e-2,
        'sc_accuracy_eev':           1e-2,
        'sc_accuracy_etot':          1e-4,
        'sc_accuracy_forces':        1e-2,
        'sc_accuracy_stress':        1e-1,
        'compute_forces':            True,
        'compute_analytical_stress': True,
        'KS_method':                 'parallel',
        'k_grid_density':            1e-1,
        'calculator_exec':           'srun -n2 --exclusive --mem=2GB aims.210313.scalapack.mpi.x',
        }


calculator = (Aims, [], aims_kwargs)


generic.calculate(
    inputs = inputs,
    outputs = outputs,
    calculator = calculator,
    properties = ["energy", "forces", "stress"],
    output_prefix = "FHIaims_",
    autopara_info = AutoparaInfo(
        num_python_subprocesses = 4,
        num_inputs_per_python_subprocess=1
    )
)