# coding: utf-8
# Copyright (c) Pymatgen Development Team.
# Distributed under the terms of the MIT License.
"""
Factory functions producing ABINIT Works.
Works are packed together in a flow. A flow can be ran using abirun (abipy)
Entry points for client code (high-level interface)
"""
import os
from .abiobjects import KSampling, Screening, SelfEnergy, ExcHamiltonian, HilbertTransform
from .works import BandStructureWork, G0W0Work, BseMdfWork
__author__ = "Matteo Giantomassi"
__copyright__ = "Copyright 2013, The Materials Project"
__version__ = "0.1"
__maintainer__ = "Matteo Giantomassi"
__email__ = "gmatteo at gmail.com"
[docs]def g0w0_extended_work(structure, pseudos, kppa, nscf_nband, ecuteps, ecutsigx, scf_nband, accuracy="normal",
spin_mode="polarized", smearing="fermi_dirac:0.1 eV", response_models=["godby"], charge=0.0,
inclvkb=2, scr_nband=None, sigma_nband=None, workdir=None, manager=None, gamma=True, nksmall=20,
work_class=None, **extra_abivars):
"""
Returns a :class:`Work` object that performs G0W0 calculations for the given the material.
Args:
structure: Pymatgen structure.
pseudos: List of `Pseudo` objects.
scf_ Defines the sampling used for the SCF run.
nscf_nband: Number of bands included in the NSCF run.
ecuteps: Cutoff energy [Ha] for the screening matrix.
ecutsigx: Cutoff energy [Ha] for the exchange part of the self-energy.
accuracy: Accuracy of the calculation.
spin_mode: Spin polarization.
smearing: Smearing technique.
ppmodel: Plasmonpole technique.
charge: Electronic charge added to the unit cell.
scf_algorithm: Algorithm used for solving of the SCF cycle.
inclvkb: Treatment of the dipole matrix elements (see abinit variable).
scr_nband: Number of bands used to compute the screening (default is nscf_nband)
sigma_nband: Number of bands used to compute the self-energy (default is nscf_nband)
workdir: Working directory.
manager: :class:`TaskManager` instance.
nksamll: if not None, a DFT bandstucture calculation will be added after the sc run
extra_abivars: Dictionary with extra variables passed to ABINIT.
"""
# TODO: Cannot use istwfk != 1.
# all these too many options are for development only the current idea for the final version is
# if gamma:
# scf_ksampling = KSampling.automatic_density(structure=structure, kppa=10000, chksymbreak=0, shifts=(0, 0, 0))
# nscf_ksampling = KSampling.gamma_centered(kpts=(2, 2, 2))
# if kppa <= 13:
# nscf_ksampling = KSampling.gamma_centered(kpts=(scf_kppa, scf_kppa, scf_kppa))
# else:
# nscf_ksampling = KSampling.automatic_density(structure, scf_kppa, chksymbreak=0, shifts=(0, 0, 0))
# else:
# scf_ksampling = KSampling.automatic_density(structure, scf_kppa, chksymbreak=0)
# nscf_ksampling = KSampling.automatic_density(structure, scf_kppa, chksymbreak=0)
if gamma:
if kppa == 1:
scf_ksampling = KSampling.gamma_centered(kpts=(1, 1, 1))
nscf_ksampling = KSampling.gamma_centered(kpts=(1, 1, 1))
elif kppa == 2:
scf_ksampling = KSampling.gamma_centered(kpts=(2, 2, 2))
nscf_ksampling = KSampling.gamma_centered(kpts=(2, 2, 2))
elif kppa < 0:
scf_ksampling = KSampling.gamma_centered(kpts=(-kppa, -kppa, -kppa))
nscf_ksampling = KSampling.gamma_centered(kpts=(2, 2, 2))
elif kppa <= 13:
scf_ksampling = KSampling.gamma_centered(kpts=(kppa, kppa, kppa))
nscf_ksampling = KSampling.gamma_centered(kpts=(kppa, kppa, kppa))
else:
scf_ksampling = KSampling.automatic_density(structure, kppa, chksymbreak=0, shifts=(0, 0, 0))
nscf_ksampling = KSampling.automatic_density(structure, kppa, chksymbreak=0, shifts=(0, 0, 0))
else:
# this is the original behaviour before the devellopment of the gwwrapper
scf_ksampling = KSampling.automatic_density(structure, kppa, chksymbreak=0)
nscf_ksampling = KSampling.automatic_density(structure, kppa, chksymbreak=0)
print(scf_ksampling)
print(nscf_ksampling)
if "istwfk" not in extra_abivars:
extra_abivars["istwfk"] = "*1"
scf_inputs = []
to_add = {}
# scf_nband = min(nscf_nband)
# print(scf_nband)
extra_abivars.update(to_add)
for k in extra_abivars.keys():
if k[-2:] == '_s':
var = k[:len(k) - 2]
values = extra_abivars.pop(k)
to_add.update({k: values[-1]})
for value in values:
extra_abivars[var] = value
extra_abivars['pawecutdg'] = extra_abivars['ecut'] * 2
scf_inputs.append(ScfStrategy(structure, pseudos, scf_ksampling, accuracy=accuracy,
spin_mode=spin_mode, smearing=smearing, charge=charge,
scf_algorithm=None, nband=scf_nband, **extra_abivars))
# temporary for testing a new approach ...
spread_scr = False if os.path.isfile('no_spread_scr') else True
if len(scf_strategy) == 0:
scf_strategy.append(ScfStrategy(structure, pseudos, scf_ksampling, accuracy=accuracy, spin_mode=spin_mode,
smearing=smearing, charge=charge, scf_algorithm=None, nband=scf_nband,
**extra_abivars))
nscf_strategy = NscfStrategy(scf_strategy[-1], nscf_ksampling, int(max(nscf_nband) * 1.1) + 1,
nbdbuf=int(0.1 * max(nscf_nband)), nstep=200, **extra_abivars)
if scr_nband is None:
scr_nband = nscf_nband
if sigma_nband is None:
sigma_nband = nscf_nband
if ecutsigx < max(ecuteps):
ecutsigx = max(ecuteps)
sigma_strategy = []
if 'cd' in response_models:
hilbert = HilbertTransform(nomegasf=100, domegasf=None, spmeth=1, nfreqre=None, freqremax=None, nfreqim=None,
freqremin=None)
for response_model in response_models:
for ecuteps_v in ecuteps:
for nscf_nband_v in nscf_nband:
scr_nband = nscf_nband_v
sigma_nband = nscf_nband_v
if response_model == 'cd':
screening = Screening(ecuteps_v, scr_nband, w_type="RPA", sc_mode="one_shot", hilbert=hilbert,
ecutwfn=None, inclvkb=inclvkb)
self_energy = SelfEnergy("gw", "one_shot", sigma_nband, ecutsigx, screening, hilbert=hilbert)
else:
ppmodel = response_model
screening = Screening(ecuteps_v, scr_nband, w_type="RPA", sc_mode="one_shot", ecutwfn=None,
inclvkb=inclvkb)
self_energy = SelfEnergy("gw", "one_shot", sigma_nband, ecutsigx, screening, ppmodel=ppmodel,
gw_qprange=1)
scr_strategy = ScreeningStrategy(scf_strategy[-1], nscf_strategy, screening, **extra_abivars)
sigma_strategy.append(SelfEnergyStrategy(scf_strategy[-1], nscf_strategy, scr_strategy, self_energy,
**extra_abivars))
if work_class is None:
work_class = G0W0Work
print(work_class)
return work_class(scf_strategy, nscf_strategy, scr_strategy, sigma_strategy, workdir=workdir, manager=manager,
spread_scr=spread_scr, nksmall=nksmall)