Flow to analyze the convergence of phonons in metals wrt ngkpt and tsmear

This examples shows how to build a Flow to compute the phonon band structure in a metallic system (MgB2) with different k-point samplings and values of the electronic smearing tsmear

import os
import sys
import abipy.data as abidata
import abipy.abilab as abilab
from abipy import flowtk

def make_scf_input(structure, ngkpt, tsmear, pseudos, paral_kgb=1):
    """Build and return Ground-state input for MgB2 given ngkpt and tsmear."""

    scf_inp = abilab.AbinitInput(structure, pseudos=pseudos)

    # Global variables
        occopt=4,    # Marzari smearing

    # Dataset 1 (GS run)
    scf_inp.set_kmesh(ngkpt=ngkpt, shiftk=structure.calc_shiftk())

    return scf_inp

def build_flow(options):
    # Working directory (default is the name of the script with '.py' removed and "run_" replaced by "flow_")
    if not options.workdir:
        options.workdir = os.path.basename(sys.argv[0]).replace(".py", "").replace("run_", "flow_")

    structure = abidata.structure_from_ucell("MgB2")

    # Get pseudos from a table.
    table = abilab.PseudoTable(abidata.pseudos("12mg.pspnc", "5b.pspnc"))
    pseudos = table.get_pseudos_for_structure(structure)

    flow = flowtk.Flow(workdir=options.workdir)

    # Build work of GS task. Each gs_task uses different (ngkpt, tsmear) values
    # and represent the starting point of the phonon works.
    scf_work = flowtk.Work()
    ngkpt_list = [[4, 4, 4], [8, 8, 8]] #, [12, 12, 12]]
    tsmear_list = [0.01, 0.02] # , 0.04]
    for ngkpt in ngkpt_list:
        for tsmear in tsmear_list:
            scf_input = make_scf_input(structure, ngkpt, tsmear, pseudos)

    # This call uses the information reported in the GS task to
    # compute all the independent atomic perturbations corresponding to a [2, 2, 2] q-mesh.
    # For each GS task, construct a phonon work that will inherit (ngkpt, tsmear) from scf_task.
    for scf_task in scf_work:
        ph_work = flowtk.PhononWork.from_scf_task(scf_task, qpoints=[2, 2, 2], is_ngqpt=True)

    return flow.allocate(use_smartio=True)

# This block generates the thumbnails in the AbiPy gallery.
# You can safely REMOVE this part if you are using this script for production runs.
if os.getenv("READTHEDOCS", False):
    __name__ = None
    import tempfile
    options = flowtk.build_flow_main_parser().parse_args(["-w", tempfile.mkdtemp()])

def main(options):
    This is our main function that will be invoked by the script.
    flow_main is a decorator implementing the command line interface.
    Command line args are stored in `options`.
    return build_flow(options)

if __name__ == "__main__":


/Users/gmatteo/git_repos/pymatgen/pymatgen/util/plotting.py:550: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure.

Run the script with:

run_mgb2_phonons_nkpt_tsmear.py -s

then use:

abicomp.py ddb flow_mgb2_phonons_nkpt_tsmear/w*/outdata/*_DDB -ipy

to build a robot from the output DDB files and start the ipython shell.

then, inside the ipython shell, use:

In [1]: %matplotlib
In [2]: r = robot.anaget_phonon_plotters(nqsmall=0)
In [3]: r.phbands_plotter.gridplot_with_hue("tsmear")

to compute the phonon bands with Anaddb and plot the results grouped by “tsmear”.

Convergence of phonon dispersion of MgB2 wrt k-point sampling and electronic smearing.

Total running time of the script: ( 0 minutes 4.491 seconds)

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