Band structure and density of states

Note

This notebook can be downloaded as silicon_band_dos.ipynb and silicon_band_dos.md

In this example we will perform band structure and DOS calculation for silicon using VASP. We will use the VaspBandWorkChain from the aiida-vasp plugin.

It is recommended to go through the single point calculation tutorial first before proceeding with this example.

Setting up the environment

The code block below configures the environment for this example. Please see the single point example for more details.

from aiida_vasp.utils.temp_profile import *
from subprocess import check_output
print(load_temp_profile())


# Uncomment the below line to create a localhost Computer if you have not done so
comp = orm.Computer('localhost', 'localhost', transport_type='core.local', scheduler_type='core.direct')
comp.store()


# Some configuration may be needed for first-time user
import os
from pathlib import Path
comp.set_workdir('/tmp/aiida_run/')
comp.configure()
vasp_path = check_output(['which', 'mock-vasp'], universal_newlines=True).strip()

vasp_code = orm.InstalledCode(comp, vasp_path, default_calc_job_plugin='vasp.vasp')
vasp_code.label ='mock-vasp'
vasp_code.store()
os.environ['MOCK_VASP_REG_BASE'] = str((Path() / 'mock_registry').absolute())
os.environ['MOCK_VASP_UPLOAD_PREFIX'] = 'band'

# Upload the POTCAR files
from aiida_vasp.data.potcar import PotcarData, PotcarFileData
from pathlib import Path

print(PotcarData.upload_potcar_family(str(Path('potcars').absolute()), "PBE.EXAMPLE", "PBE.EXAMPLE"))

# Setting up the silicon structure
from ase.build import bulk
si = bulk('Si', 'diamond', 5.4)
si_node = orm.StructureData(ase=si)

Profile<uuid='a108ce48dae342fea4c164c0ba152165' name='myprofile'>

(1, 1, 1)

Setting up the band structure and DOS calculation

Similar to the single point calculation tutorial, we will use a VaspInputGenerator to setup the inputs for the VaspBandsWorkChain:

from aiida_vasp.workchains.v2.bands import BandOptions
from aiida_vasp.protocols.generator import VaspBandsInputGenerator

upd = VaspBandsInputGenerator()
builder = upd.get_builder(si_node, code='mock-vasp@localhost',
                          run_relax=False,
                          overrides={
                            'scf':
                            {'potential_family': 'PBE.EXAMPLE'},
                            })

The workchain can be modified with several options. These options are stored in the the band_settings input node which of the type orm.Dict. The available options can be printed using the aiida_description() method.

opt = BandOptions()
print(opt.aiida_description())
# Or by accessing the help with
?builder.band_settings

                              symprec:  float      
                                        Default: Precision of the symmetry determination
                            band_mode:  str        
                                        Default: Mode for generating the band path. Choose from: bradcrack, pymatgen,seekpath-aiida and latimer-munro.
                band_kpoints_distance:  float      
                                        Default: Spacing for band distances for automatic kpoints generation, used by seekpath-aiida mode.
                         line_density:  float      
                                        Default: Density of the point along the path, used by the sumo interface.
                 dos_kpoints_distance:  float      
                                        Default: Kpoints for running DOS calculations in A^-1 * 2pi. Will perform non-SCF DOS calculation is supplied.
                             only_dos:  bool       
                                        Default: Flag for running only DOS calculations
                              run_dos:  bool       
                                        Default: Flag for running DOS calculations
  additional_band_analysis_parameters:  dict       
                                        Default: Additional keyword arguments for the seekpath/ interface, available keys are:  ['with_time_reversal', 'reference_distance', 'recipe', 'threshold', 'symprec', 'angle_tolerance']
                    kpoints_per_split:  int        
                                        Default: Number of kpoints per split for the band structure calculation
                 hybrid_reuse_wavecar:  bool       
                                        Default: Whether to reuse the WAVECAR from the previous relax/singlepoint calculation

Hint

This can also be done for other Option classes such as relax_settings for the VaspRelaxWorkChain which uses the RelaxOptions class.

Run and inspect the results

We can now run the workchain and get the returned WorkChainNode object.

band_out = upd.run_get_node().node

The computed band structure is stored as a BandsData object in the band_structure output port. We can plot the band structure using the show_mpl method provided.

band_out.outputs.band_structure.show_mpl()

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