Espresso¶

Quantum ESPRESSO (QE) is an integrated suite of Open-Source computer codes for electronic-structure calculations and materials modeling at the nanoscale. It is based on density-functional theory, plane waves, and pseudopotentials.

The ASE calculator is an interface to the pw.x executable.

Calculations using the Espresso calculator can be run by defining an EspressoProfile and setting up an Espresso calculator with it:

from ase.calculators.espresso import Espresso, EspressoProfile

espresso_profile = EspressoProfile(["pw.x"])
calc = Espresso(profile=espresso_profile)

The above calc object will run the pw.x executable on 1 core. The argument to the EspressoProfile object is a list of tokens that can be executed in the shell to run QE as desired. An example of such a list would be:

["mpirun", "-np", "16", "pw.x"] # Run QE on 16 cores

Lists like the above can be obtained conveniently using string splitting. Below we provide a few examples of QE run commands:

"mpirun -np 16 pw.x" # Run QE on 16 cores, with default parallelism.
"mpirun -np 64 pw.x -nk 2" # Run QE on 64 cores, parallel over 2 kpoints.

# As above, diagonalize using Scalapack/Elpa
# (if your QE was installed with support) on an 8x8
# grid of processors.
"mpirun -np 64 pw.x -ndiag 64"

For calculations one would define the profile and the calculator as:

from ase.calculators.espresso import Espresso, EspressoProfile

NSLOTS = 64
espresso_profile = EspressoProfile(f"mpirun -np {NSLOTS} pw.x".split())

# This line is incomplete, since the user must also provide calculation
# inputs before a successful calculation can be run.
calc = Espresso(profile=espresso_profile)

Any calculation will need pseudopotentials for the elements involved. The directory for the pseudopotential files can be set with the pseudo_dir parameter, otherwise QE will look in $ESPRESSO_PSEUDO if it is set as an environment variable if set; otherwise $HOME/espresso/pseudo/ is used. The pseudopotentials are assigned for each element as a dictionary:

pseudopotentials = {'Na': 'na_pbe_v1.5.uspp.F.UPF',
                    'Cl': 'cl_pbe_v1.4.uspp.F.UPF'}

A recommended list of pseudopotentials is the SSSP curated set, which can be found on Materials Cloud.

A simple calculation can be set up like so:

from ase.build import bulk
from ase.calculators.espresso import Espresso, EspressoProfile
from ase.constraints import ExpCellFilter
from ase.optimize import LBFGS

rocksalt = bulk('NaCl', crystalstructure='rocksalt', a=6.0)

# Pseudopotentials from SSSP Efficiency v1.3.0
pseudopotentials = {'Na': 'na_pbe_v1.5.uspp.F.UPF',
                    'Cl': 'cl_pbe_v1.4.uspp.F.UPF'}

calc = Espresso(profile=EspressoProfile("mpirun -np 4 pw.x".split()),
                ecutwfc=25, # This is unconverged and used for this example
                pseudopotentials=pseudopotentials,
                pseudo_dir=path_to_pseudopotentials,
                tstress=True, tprnfor=True, kpts=(3, 3, 3))
rocksalt.calc = calc

filt = ExpCellFilter(rocksalt)
opt = LBFGS(filt)
opt.run(fmax=0.005)

# cubic lattic constant
print((8*rocksalt.get_volume()/len(rocksalt))**(1.0/3.0))

Parameters¶

The calculator will interpret most documented options for pw.x found at the PW input description, current exceptions being (at least) the HUBBARD, &FCP, &RISM sections.

All parameters must be given in QE units, usually Ry or atomic units in line with the documentation. ASE does not add any defaults over the defaults of QE.

Parameters can be given as keywords and the calculator will put them into the correct section of the input file. The calculator also accepts a keyword argument input_data which is a dict, parameters may be put into sections in input_data, but it is not necessary:

input_data = {
    'system': {
      'ecutwfc': 64,
      'ecutrho': 576}
    'disk_io': 'low'}  # automatically put into 'control'

calc = Espresso(profile=my_predefined_profile,
                pseudopotentials=pseudopotentials,
                # Optionally, if the ESPRESSO_PSEUDO env var isn't set
                pseudo_dir=path_to_pseudopotentials,
                tstress=True, tprnfor=True,  # kwargs added to parameters
                input_data=input_data)

Some parameters are used by ASE, or have additional meaning:

kpts, is used to specify the k-point sampling * If kpts is a tuple (or list) of 3 integers kpts=(int, int, int), it is interpreted as the dimensions of a Monkhorst-Pack grid. * If kpts is set to None, only the Γ-point will be included and QE will use routines optimized for Γ-point-only calculations. Compared to Γ-point-only calculations without this optimization (i.e. with kpts=(1, 1, 1)), the memory and CPU requirements are typically reduced by half. * If kpts is a dict, it will either be interpreted as a path in the Brillouin zone (see ase.dft.kpoints.bandpath) if it contains the ‘path’ keyword, otherwise it is converted to a Monkhorst-Pack grid (see ase.calculators.calculator.kpts2sizeandoffsets)

koffset=(0, 0, 0) set to 0 or 1 to displace the kpoint grid by a half cell in that direction. Also accepts True and False.

kspacing=0.1 sets the minimum distance between kpoints in reciprocal space.

nspin=2 if any atom has a magnetic moment spin is turned on automatically.

Any FixAtoms or FixCartesian constraints are converted to Espresso constraints (for dynamic calculations).

Alternative Calculators¶

There are several other QE Calculator implementations based on ase that provide a number of extra features:

http://jochym.github.io/qe-util/

https://github.com/vossjo/ase-espresso

Espresso Calculator Class¶

class ase.calculators.espresso.Espresso(*, profile=None, command=<object object>, label=<object object>, directory='.', parallel_info=None, parallel=True, **kwargs)[source]¶

All options for pw.x are copied verbatim to the input file, and put into the correct section. Use input_data for parameters that are already in a dict, all other kwargs are passed as parameters.

Accepts all the options for pw.x as given in the QE docs, plus some additional options:

input_data: dict: A flat or nested dictionary with input parameters for pw.x
pseudopotentials: dict: A filename for each atomic species, e.g. {'O': 'O.pbe-rrkjus.UPF', 'H': 'H.pbe-rrkjus.UPF'}. A dummy name will be used if none are given.
kspacing: float: Generate a grid of k-points with this as the minimum distance, in A^-1 between them in reciprocal space. If set to None, kpts will be used instead.
kpts: (int, int, int), dict, or BandPath: If kpts is a tuple (or list) of 3 integers, it is interpreted as the dimensions of a Monkhorst-Pack grid. If kpts is set to None, only the Γ-point will be included and QE will use routines optimized for Γ-point-only calculations. Compared to Γ-point-only calculations without this optimization (i.e. with kpts=(1, 1, 1)), the memory and CPU requirements are typically reduced by half. If kpts is a dict, it will either be interpreted as a path in the Brillouin zone (*) if it contains the ‘path’ keyword, otherwise it is converted to a Monkhorst-Pack grid (**). (*) see ase.dft.kpoints.bandpath (**) see ase.calculators.calculator.kpts2sizeandoffsets
koffset: (int, int, int): Offset of kpoints in each direction. Must be 0 (no offset) or 1 (half grid offset). Setting to True is equivalent to (1, 1, 1).

Note

Set tprnfor=True and tstress=True to calculate forces and stresses.

Note

Band structure plots can be made as follows:

Perform a regular self-consistent calculation, saving the wave functions at the end, as well as getting the Fermi energy:

>>> input_data = {<your input data>}
>>> calc = Espresso(input_data=input_data, ...)
>>> atoms.calc = calc
>>> atoms.get_potential_energy()
>>> fermi_level = calc.get_fermi_level()

Perform a non-self-consistent ‘band structure’ run after updating your input_data and kpts keywords:

>>> input_data['control'].update({'calculation':'bands',
>>>                               'restart_mode':'restart',
>>>                               'verbosity':'high'})
>>> calc.set(kpts={<your Brillouin zone path>},
>>>          input_data=input_data)
>>> calc.calculate(atoms)

Make the plot using the BandStructure functionality, after setting the Fermi level to that of the prior self-consistent calculation:
```
>>> bs = calc.band_structure()
>>> bs.reference = fermi_energy
>>> bs.plot()
```