# 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)
"""
from __future__ import unicode_literals, division, print_function
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"
#def bandstructure_work(structure, pseudos, scf_kppa, nscf_nband,
# ndivsm, accuracy="normal", spin_mode="polarized",
# smearing="fermi_dirac:0.1 eV", charge=0.0, scf_algorithm=None,
# dos_kppa=None, workdir=None, manager=None, work_class=None, **extra_abivars):
# """
# Returns a :class:`Work` for bandstructure calculations.
#
# Args:
# structure: Pymatgen structure.
# pseudos: List of `Pseudo` objects.
# scf_kppa: Defines the sampling used for the SCF run.
# nscf_nband: Number of bands included in the NSCF run.
# ndivs: Number of divisions used to sample the smallest segment of the k-path.
# accuracy: Accuracy of the calculation.
# spin_mode: Spin polarization.
# smearing: Smearing technique.
# charge: Electronic charge added to the unit cell.
# scf_algorithm: Algorithm used for solving of the SCF cycle.
# dos_kppa: Defines the k-point sampling used for the computation of the DOS
# (None if DOS is not wanted).
# workdir: Working directory.
# manager: :class:`TaskManager` instance.
# extra_abivars: Dictionary with extra variables passed to ABINIT.
# """
# #multi = MultiDataset(structure, pseudos, ndtset=2 if dos_kppa is None else 2 + len(dos_kppa))
#
# # SCF calculation.
# scf_ksampling = KSampling.automatic_density(structure, scf_kppa, chksymbreak=0)
#
# scf_strategy = ScfStrategy(structure, pseudos, scf_ksampling,
# accuracy=accuracy, spin_mode=spin_mode,
# smearing=smearing, charge=charge,
# scf_algorithm=scf_algorithm, **extra_abivars)
#
# #scf_electrons = Electrons(spin_mode=spin_mode, smearing=smearing, algorithm=scf_algorithm,
# # charge=charge, nband=scf_nband, fband=None)
# #multi[0].set_vars(scf_ksampling.to_abivars())
# #multi[0].set_vars(scf_electrons.to_abivars())
#
# # Band structure calculation.
# nscf_ksampling = KSampling.path_from_structure(ndivsm, structure)
#
# nscf_strategy = NscfStrategy(scf_strategy, nscf_ksampling, nscf_nband, **extra_abivars)
#
# # DOS calculation.
# dos_strategy = None
# if dos_kppa is not None:
# dos_ksampling = KSampling.automatic_density(structure, dos_kppa, chksymbreak=0)
# #dos_ksampling = KSampling.monkhorst(dos_ngkpt, shiftk=dos_shiftk, chksymbreak=0)
# dos_strategy = NscfStrategy(scf_strategy, dos_ksampling, nscf_nband, nscf_solver=None, **extra_abivars)
# #dos_electrons = aobj.Electrons(spin_mode=spin_mode, smearing=smearing, algorithm={"iscf": -2},
# # charge=charge, nband=nscf_nband)
#
# #dt = 2 + i
# #multi[dt].set_vars(dos_ksampling.to_abivars())
# #multi[dt].set_vars(dos_electrons.to_abivars())
# #multi[dt].set_vars(_stopping_criterion("nscf", accuracy))
#
# if work_class is None: work_class = BandStructureWork
# return work_class(scf_strategy, nscf_strategy, dos_inputs=dos_strategy, workdir=workdir, manager=manager)
#
#
#def g0w0_with_ppmodel_work(structure, pseudos, scf_kppa, nscf_nband, ecuteps, ecutsigx,
# accuracy="normal", spin_mode="polarized", smearing="fermi_dirac:0.1 eV",
# ppmodel="godby", charge=0.0, scf_algorithm=None, inclvkb=2, scr_nband=None,
# sigma_nband=None, gw_qprange=1, workdir=None, manager=None, 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_kppa: 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)
# gw_qprange: Option for the automatic selection of k-points and bands for GW corrections.
# See Abinit docs for more detail. The default value makes the code compute the
# QP energies for all the point in the IBZ and one band above and one band below the Fermi level.
# workdir: Working directory.
# manager: :class:`TaskManager` instance.
# extra_abivars: Dictionary with extra variables passed to ABINIT.
# """
# # TODO: Cannot use istwfk != 1.
# if "istwfk" not in extra_abivars:
# extra_abivars["istwfk"] = "*1"
#
# scf_ksampling = KSampling.automatic_density(structure, scf_kppa, chksymbreak=0)
#
# scf_strategy = ScfStrategy(structure, pseudos, scf_ksampling,
# accuracy=accuracy, spin_mode=spin_mode,
# smearing=smearing, charge=charge,
# scf_algorithm=scf_algorithm, **extra_abivars)
#
# nscf_ksampling = KSampling.automatic_density(structure, scf_kppa, chksymbreak=0)
#
# nscf_strategy = NscfStrategy(scf_strategy, nscf_ksampling, nscf_nband, **extra_abivars)
#
# if scr_nband is None: scr_nband = nscf_nband
# if sigma_nband is None: sigma_nband = nscf_nband
#
# screening = Screening(ecuteps, scr_nband, w_type="RPA", sc_mode="one_shot",
# hilbert=None, ecutwfn=None, inclvkb=inclvkb)
#
# self_energy = SelfEnergy("gw", "one_shot", sigma_nband, ecutsigx, screening,
# gw_qprange=gw_qprange, ppmodel=ppmodel)
#
# scr_strategy = ScreeningStrategy(scf_strategy, nscf_strategy, screening, **extra_abivars)
#
# sigma_strategy = SelfEnergyStrategy(scf_strategy, nscf_strategy, scr_strategy, self_energy,
# **extra_abivars)
#
# if work_class is None: work_class = G0W0Work
# return work_class(scf_strategy, nscf_strategy, scr_strategy, sigma_strategy, workdir=workdir, manager=manager)
[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)
#def bse_with_mdf_work(structure, pseudos, scf_kppa, nscf_nband, nscf_ngkpt, nscf_shiftk,
# ecuteps, bs_loband, bs_nband, soenergy, mdf_epsinf,
# exc_type="TDA", bs_algo="haydock", accuracy="normal", spin_mode="polarized",
# smearing="fermi_dirac:0.1 eV", charge=0.0, scf_algorithm=None, workdir=None, manager=None,
# work_class=None, **extra_abivars):
# """
# Returns a :class:`Work` object that performs a GS + NSCF + Bethe-Salpeter calculation.
# The self-energy corrections are approximated with the scissors operator.
# The screening in modeled by the model dielectric function.
#
# Args:
# structure: :class:`Structure` object.
# pseudos: List of `Pseudo` objects.
# scf_kppa: Defines the sampling used for the SCF run.
# nscf_nband: Number of bands included in the NSCF run.
# nscf_ngkpt: Divisions of the k-mesh used for the NSCF and the BSE run.
# nscf_shiftk: Shifts used for the NSCF and the BSE run.
# ecuteps: Cutoff energy [Ha] for the screening matrix.
# bs_loband: Index of the first occupied band included the e-h basis set
# (ABINIT convention i.e. first band starts at 1).
# Can be scalar or array of shape (nsppol,)
# bs_nband: Highest band idex used for the construction of the e-h basis set.
# soenergy: Scissor energy in Hartree.
# mdf_epsinf: Value of the macroscopic dielectric function used in expression for the model dielectric function.
# exc_type: Approximation used for the BSE Hamiltonian (Tamm-Dancoff or coupling).
# bs_algo: Algorith for the computatio of the macroscopic dielectric function.
# accuracy: Accuracy of the calculation.
# spin_mode: Spin polarization.
# smearing: Smearing technique.
# charge: Electronic charge added to the unit cell.
# scf_algorithm: Algorithm used for solving the SCF cycle.
# workdir: Working directory.
# manager: :class:`TaskManger` instance.
# extra_abivars: Dictionary with extra variables passed to ABINIT.
# """
# # TODO: Cannot use istwfk != 1.
# if "istwfk" not in extra_abivars:
# extra_abivars["istwfk"] = "*1"
#
# # Ground-state strategy.
# scf_ksampling = KSampling.automatic_density(structure, scf_kppa, chksymbreak=0)
#
# scf_strategy = ScfStrategy(structure, pseudos, scf_ksampling,
# accuracy=accuracy, spin_mode=spin_mode,
# smearing=smearing, charge=charge, scf_algorithm=None, **extra_abivars)
#
# # NSCF calculation with the randomly-shifted k-mesh.
# nscf_ksampling = KSampling.monkhorst(nscf_ngkpt, shiftk=nscf_shiftk, chksymbreak=0)
#
# nscf_strategy = NscfStrategy(scf_strategy, nscf_ksampling, nscf_nband, **extra_abivars)
#
# # Strategy for the BSE calculation.
# exc_ham = ExcHamiltonian(bs_loband, bs_nband, soenergy, coulomb_mode="model_df", ecuteps=ecuteps,
# spin_mode=spin_mode, mdf_epsinf=mdf_epsinf, exc_type=exc_type, algo=bs_algo,
# bs_freq_mesh=None, with_lf=True, zcut=None)
#
# bse_strategy = MdfBse_Strategy(scf_strategy, nscf_strategy, exc_ham, **extra_abivars)
#
# if work_class is None: work_class = BseMdfWork
# return work_class(scf_strategy, nscf_strategy, bse_strategy, workdir=workdir, manager=manager)