Note
Go to the end to download the full example code.
GWR flow with convergence studies
This script shows how to compute the G0W0 corrections in silicon. More specifically, we build a flow to analyze the convergence of the QP corrections wrt to the number of bands in the self-energy. More complicated convergence studies can be implemented on the basis of this example.
Downloading repository from: https://github.com/PseudoDojo/ONCVPSP-PBE-PDv0.4/archive/refs/heads/master.zip ...
Installing ONCVPSP-PBE-SR-PDv0.4 in: /home/runner/.abinit/pseudos/ONCVPSP-PBE-SR-PDv0.4
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Validating md5 checksums of ONCVPSP-PBE-SR-PDv0.4...
Checksum test: OK
Installation completed successfully in 16.21 [s]
import os
import sys
from abipy import abilab, data, flowtk
from abipy.flowtk.gwr_works import DirectDiagoWork, GWRSigmaConvWork
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_")
# IMPORTANT: Note stringent table to have semi-core states.
from abipy.flowtk.psrepos import get_oncvpsp_pseudos
pseudos = get_oncvpsp_pseudos(xc_name="PBE", version="0.4", relativity_type="SR", accuracy="stringent")
scf_input = abilab.AbinitInput(structure=data.cif_file("si.cif"), pseudos=pseudos)
num_ele = scf_input.num_valence_electrons
# Global variables.
scf_input.set_vars(
ecut=6,
nband=num_ele // 2,
tolvrs=1e-8,
paral_kgb=1,
)
# IMPORTANT: k-grid for GWR must be Gamma-centered.
scf_input.set_kmesh(ngkpt=[2, 2, 2], shiftk=[0.0, 0.0, 0.0])
flow = flowtk.Flow(workdir=options.workdir)
# GS-SCF run to get the DEN, followed by direct diago to obtain green_nband bands.
green_nband = -1 # -1 this means full diago
diago_work = DirectDiagoWork.from_scf_input(scf_input, green_nband)
flow.register_work(diago_work)
# Build template for GWR.
gwr_template = scf_input.make_gwr_qprange_input(gwr_ntau=6, nband=8, ecuteps=4, ecutwfn=2)
# Two possibilities:
# 1) To change the value of one variable, use:
varname_values = ("nband", [8, 12, 14])
# 2) To take the Cartesian product of two or more variables use e.g.:
#
# varname_values = [
# ("nband", [50, 100]),
# ("ecuteps", [2, 4]),
# ]
# Can also use strings with path to files for den_node and wfk_node
# so that one does not need to recompute these files.
gwr_work = GWRSigmaConvWork.from_varname_values(
varname_values,
gwr_template,
den_node=diago_work.scf_task,
wfk_node=diago_work.diago_task,
)
flow.register_work(gwr_work)
return flow
# 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()])
build_flow(options).graphviz_imshow()
@flowtk.flow_main
def main(options):
return build_flow(options)
if __name__ == "__main__":
sys.exit(main())
Total running time of the script: (0 minutes 16.554 seconds)