Calculate a band structure

The PwBandsWorkChain is designed to compute electronic band structures for a given structure using Quantum ESPRESSO’s pw.x. It automates the complete workflow, starting from a previously relaxed structure then it performs an SCF calculation, and bands calculation along high-symmetry k-points paths.

Workchain class	aiida_quantumespresso.workflows.pw.bands.PwBandsWorkChain
Workchain entry point	quantumespresso.pw.bands

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Minimal example: build and submit

from aiida import orm, load_profile
from aiida_quantumespresso.workflows.pw.bands import PwBandsWorkChain
from aiida.engine import submit
from ase.build import bulk

load_profile()

# Load your pw.x code and structure
code = orm.load_code('pw@localhost')
structure = orm.StructureData(ase=bulk('Si', 'diamond', 5.4))

# Get a builder with sensible default parameters from a predefined protocol
builder = PwBandsWorkChain.get_builder_from_protocol(
    code=code,
    structure=structure,
    protocol="balanced",  # choose from: fast, balanced, stringent
    options={
        "account": "your_account", # Change to your account if needed by your HPC provider. Otherwise, remove this line.
        "queue_name": "debug",
        "resources": {"num_machines": 1},
        "max_wallclock_seconds": 1800,
    }
)

# Submit the work chain
workchain_node = submit(builder)
print(f"Launched {workchain_node.process_label} with PK = {workchain_node.pk}")

The workchain will automatically:

1. Use SeeKpath to find the primitive cell and generate a high-symmetry k-points path.

2. Run an SCF calculation to obtain the ground state.

3. Run a bands calculation along the high-symmetry k-points path.

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Customizing the workflow

Specifying custom k-points list

You can provide your own list of k-points instead of using SeeKpath:

from aiida import orm

# Create a custom KpointsData. For example, a list of points:
kpoints = orm.KpointsData()
kpoints.set_kpoints([
    [0.0, 0.0, 0.0],
    [0.1, 0.0, 0.0],
    [0.2, 0.0, 0.0],
    # ... more k-points
])

builder.bands_kpoints = kpoints
builder.pop('bands_kpoints_distance', None) # Remove the distance input

In that case, you need to remove the bands_kpoints_distance input that was set by default by the protocol.

Adjusting the number of bands

Control the number of bands in the bands calculation using nbands_factor:

# Number of bands will be: max(nbnd_scf, 0.5 * nelectrons * factor, 0.5 * nelectrons + 4)
builder.nbands_factor = orm.Float(1.5)

Alternatively, set the number of bands directly:

builder.bands.pw.parameters['SYSTEM']['nbnd'] = 50

Note

You cannot specify both nbands_factor and bands.pw.parameters.SYSTEM.nbnd.

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Inspecting results

After the work chain finishes, the main outputs are:

• band_structure: the computed electronic band structure along the k-points path

• scf_parameters: output parameters from the SCF calculation

• band_parameters: output parameters from the bands calculation

• primitive_structure: the normalized primitive structure (if SeeKpath was used)

• seekpath_parameters: parameters from the SeeKpath analysis (if SeeKpath was used)

Visualizing the band structure

You can visualize the band structure using common plotting libraries. For example here, a simple script that shows the general structure of the bands:

import matplotlib.pyplot as plt
import numpy as np

band_structure = workchain_node.outputs.band_structure
seekpath_params = workchain_node.outputs.seekpath_parameters.get_dict()
explicit_kpoints = seekpath_params['explicit_kpoints_linearcoord']
bands = band_structure.get_bands()

for i in range(bands.shape[1]):
     plt.plot(explicit_kpoints, bands[:, i], color='blue')

Protocol details

The available protocols (See the discussion for more details ) (fast, balanced, stringent) differ in:

• Plane-wave cutoff energies

• k-points density for SCF

• k-points spacing along the bands path

• Convergence thresholds

Choose fast for quick tests, balanced for production calculations, and stringent for high-accuracy results.