def ion_ioncell_relax_input(structure, pseudos,
kppa=None, nband=None,
ecut=None, pawecutdg=None, accuracy="normal", spin_mode="polarized",
smearing="fermi_dirac:0.1 eV", charge=0.0, scf_algorithm=None):
"""
Returns a :class:`AbinitInput` for a structural relaxation. The first dataset optmizes the
atomic positions at fixed unit cell. The second datasets optimizes both ions and unit cell parameters.
Args:
structure: :class:`Structure` object.
pseudos: List of filenames or list of :class:`Pseudo` objects or :class:`PseudoTable` object.
kppa: Defines the sampling used for the Brillouin zone.
nband: Number of bands included in the SCF run.
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.
"""
structure = Structure.as_structure(structure)
multi = MultiDataset(structure, pseudos, ndtset=2)
# Set the cutoff energies.
multi.set_vars(_find_ecut_pawecutdg(ecut, pawecutdg, multi.pseudos))
kppa = _DEFAULTS.get("kppa") if kppa is None else kppa
ksampling = aobj.KSampling.automatic_density(structure, kppa, chksymbreak=0)
electrons = aobj.Electrons(spin_mode=spin_mode, smearing=smearing, algorithm=scf_algorithm,
charge=charge, nband=nband, fband=None)
if spin_mode=="polarized":
spinat_dict = multi[0].set_autospinat()
multi[1].set_vars(spinat_dict)
if electrons.nband is None:
electrons.nband = _find_scf_nband(structure, multi.pseudos, electrons, multi[0].get('spinat', None))
ion_relax = aobj.RelaxationMethod.atoms_only(atoms_constraints=None)
ioncell_relax = aobj.RelaxationMethod.atoms_and_cell(atoms_constraints=None)
multi.set_vars(electrons.to_abivars())
multi.set_vars(ksampling.to_abivars())
multi[0].set_vars(ion_relax.to_abivars())
multi[0].set_vars(_stopping_criterion("relax", accuracy))
multi[1].set_vars(ioncell_relax.to_abivars())
multi[1].set_vars(_stopping_criterion("relax", accuracy))
return multi
def phonons_from_gsinput(gs_inp, ph_ngqpt=None, with_ddk=True, with_dde=True, with_bec=False, ph_tol=None, ddk_tol=None,
dde_tol=None):
"""
Returns a :class:`AbinitInput` for performing phonon calculations.
GS input + the input files for the phonon calculation.
"""
gs_inp = gs_inp.deepcopy()
gs_inp.pop_irdvars()
if with_dde:
with_ddk = True
if with_bec:
with_ddk = True
with_dde = False
multi = []
if ph_ngqpt is None:
ph_ngqpt = np.array(gs_inp["ngkpt"])
qpoints = gs_inp.abiget_ibz(ngkpt=ph_ngqpt, shiftk=(0,0,0), kptopt=1).points
# Build the input files for the q-points in the IBZ.
# Response-function calculation for phonons.
for qpt in qpoints:
if np.allclose(qpt, 0):
if with_ddk:
multi_ddk = gs_inp.make_ddk_inputs(ddk_tol)
multi_ddk.add_tags(DDK)
multi.extend(multi_ddk)
if with_dde:
multi_dde = gs_inp.make_dde_inputs(dde_tol)
multi_dde.add_tags(DDE)
multi.extend(multi_dde)
elif with_bec:
multi_bec = gs_inp.make_bec_inputs(ph_tol)
multi_bec.add_tags(BEC)
multi.extend(multi_bec)
continue
multi_ph_q = gs_inp.make_ph_inputs_qpoint(qpt, ph_tol)
multi_ph_q.add_tags(PH_Q_PERT)
multi.extend(multi_ph_q)
multi = MultiDataset.from_inputs(multi)
multi.add_tags(PHONON)
#FIXME for the time being there could be problems in mergeddb if the kpoints grid is gamma centered or if
# if the grid is odd. Remove when mergeddb is fixed
multi.set_vars(kptopt=3)
return multi
def ion_ioncell_relax_input(structure,
pseudos,
kppa=None,
nband=None,
ecut=None,
pawecutdg=None,
accuracy="normal",
spin_mode="polarized",
smearing="fermi_dirac:0.1 eV",
charge=0.0,
scf_algorithm=None):
"""
Returns a :class:`AbinitInput` for a structural relaxation. The first dataset optmizes the
atomic positions at fixed unit cell. The second datasets optimizes both ions and unit cell parameters.
Args:
structure: :class:`Structure` object.
pseudos: List of filenames or list of :class:`Pseudo` objects or :class:`PseudoTable` object.
kppa: Defines the sampling used for the Brillouin zone.
nband: Number of bands included in the SCF run.
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.
"""
structure = Structure.as_structure(structure)
multi = MultiDataset(structure, pseudos, ndtset=2)
# Set the cutoff energies.
multi.set_vars(_find_ecut_pawecutdg(ecut, pawecutdg, multi.pseudos))
kppa = _DEFAULTS.get("kppa") if kppa is None else kppa
ksampling = aobj.KSampling.automatic_density(structure,
kppa,
chksymbreak=0)
electrons = aobj.Electrons(spin_mode=spin_mode,
smearing=smearing,
algorithm=scf_algorithm,
charge=charge,
nband=nband,
fband=None)
if electrons.nband is None:
electrons.nband = _find_scf_nband(structure, multi.pseudos, electrons)
ion_relax = aobj.RelaxationMethod.atoms_only(atoms_constraints=None)
ioncell_relax = aobj.RelaxationMethod.atoms_and_cell(
atoms_constraints=None)
multi.set_vars(electrons.to_abivars())
multi.set_vars(ksampling.to_abivars())
multi[0].set_vars(ion_relax.to_abivars())
multi[0].set_vars(_stopping_criterion("relax", accuracy))
multi[1].set_vars(ioncell_relax.to_abivars())
multi[1].set_vars(_stopping_criterion("relax", accuracy))
return multi
def phonons_from_gsinput(gs_inp, ph_ngqpt=None, with_ddk=True, with_dde=True, with_bec=False, ph_tol=None, ddk_tol=None,
dde_tol=None):
"""
Returns a :class:`AbinitInput` for performing phonon calculations.
GS input + the input files for the phonon calculation.
"""
if with_dde:
with_ddk = True
if with_bec:
with_ddk = True
with_dde = False
if ph_ngqpt is None:
qpoints = gs_inp.abiget_ibz().points
else:
qpoints = gs_inp.abiget_ibz(ngkpt=ph_ngqpt, shiftk=(0,0,0), kptopt=1).points
# Build the input files for the q-points in the IBZ.
# Response-function calculation for phonons.
multi = []
for qpt in qpoints:
if np.allclose(qpt, 0):
if with_ddk:
multi_ddk = gs_inp.make_ddk_inputs(ddk_tol)
multi_ddk.add_tags(DDK)
multi.extend(multi_ddk)
if with_dde:
multi_dde = gs_inp.make_dde_inputs(dde_tol)
multi_dde.add_tags(DDE)
multi.extend(multi_dde)
elif with_bec:
multi_bec = gs_inp.make_bec_inputs(ph_tol)
multi_bec.add_tags(BEC)
multi.extend(multi_bec)
continue
multi_ph_q = gs_inp.make_ph_inputs_qpoint(qpt, ph_tol)
multi_ph_q.add_tags(PH_Q_PERT)
multi.extend(multi_ph_q)
multi = MultiDataset.from_inputs(multi)
multi.add_tags(PHONON)
return multi
def scf_piezo_elastic_inputs(structure, pseudos, kppa, ecut=None, pawecutdg=None, scf_nband=None,
accuracy="normal", spin_mode="polarized",
smearing="fermi_dirac:0.1 eV", charge=0.0, scf_algorithm=None, ddk_tol=None, rf_tol=None):
"""
Returns a :class:`AbinitInput` for performing elastic and piezoelectric constants calculations.
GS input + the input files for the elastic and piezoelectric constants calculation.
Args:
structure: :class:`Structure` object.
pseudos: List of filenames or list of :class:`Pseudo` objects or :class:`PseudoTable` object.
kppa: Defines the sampling used for the SCF run.
ecut: cutoff energy in Ha (if None, ecut is initialized from the pseudos according to accuracy)
pawecutdg: cutoff energy in Ha for PAW double-grid (if None, pawecutdg is initialized from the
pseudos according to accuracy)
scf_nband: Number of bands for SCF run. If scf_nband is None, nband is automatically initialized
from the list of pseudos, the structure and the smearing option.
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.
ddk_tol
"""
# Build the input file for the GS run.
gs_inp = AbinitInput(structure=structure, pseudos=pseudos)
# Set the cutoff energies.
gs_inp.set_vars(_find_ecut_pawecutdg(ecut, pawecutdg, gs_inp.pseudos))
ksampling = aobj.KSampling.automatic_density(gs_inp.structure, kppa, chksymbreak=0, shifts=(0.0, 0.0, 0.0))
gs_inp.set_vars(ksampling.to_abivars())
gs_inp.set_vars(tolvrs=1.0e-18)
scf_electrons = aobj.Electrons(spin_mode=spin_mode, smearing=smearing, algorithm=scf_algorithm,
charge=charge, nband=None, fband=None)
if scf_electrons.nband is None:
scf_electrons.nband = _find_scf_nband(structure, gs_inp.pseudos, scf_electrons)
gs_inp.set_vars(scf_electrons.to_abivars())
all_inps = [gs_inp]
# Add the ddk input
ddk_inp = gs_inp.deepcopy()
ddk_inp.set_vars(
rfelfd=2, # Activate the calculation of the d/dk perturbation
rfdir=(1,1,1), # All directions
nqpt=1, # One wavevector is to be considered
qpt=(0, 0, 0), # q-wavevector.
kptopt=2, # Take into account time-reversal symmetry.
iscf=-3, # The d/dk perturbation must be treated in a non-self-consistent way
)
if ddk_tol is None:
ddk_tol = {"tolwfr": 1.0e-20}
if len(ddk_tol) != 1 or any(k not in _tolerances for k in ddk_tol):
raise ValueError("Invalid tolerance: {}".format(ddk_tol))
ddk_inp.pop_tolerances()
ddk_inp.set_vars(ddk_tol)
ddk_inp.add_tags(DDK)
all_inps.append(ddk_inp)
# Add the Response Function calculation
rf_inp = gs_inp.deepcopy()
rf_inp.set_vars(rfphon=1, # Atomic displacement perturbation
rfatpol=(1,len(gs_inp.structure)), # Perturbation of all atoms
rfstrs=3, # Do the strain perturbations
rfdir=(1,1,1), # All directions
nqpt=1, # One wavevector is to be considered
qpt=(0, 0, 0), # q-wavevector.
kptopt=2, # Take into account time-reversal symmetry.
iscf=7, # The d/dk perturbation must be treated in a non-self-consistent way
)
if rf_tol is None:
rf_tol = {"tolvrs": 1.0e-12}
if len(rf_tol) != 1 or any(k not in _tolerances for k in rf_tol):
raise ValueError("Invalid tolerance: {}".format(rf_tol))
rf_inp.pop_tolerances()
rf_inp.set_vars(rf_tol)
rf_inp.add_tags([DFPT, STRAIN])
all_inps.append(rf_inp)
return MultiDataset.from_inputs(all_inps)
def scf_phonons_inputs(structure, pseudos, kppa,
ecut=None, pawecutdg=None, scf_nband=None, accuracy="normal", spin_mode="polarized",
smearing="fermi_dirac:0.1 eV", charge=0.0, scf_algorithm=None):
"""
Returns a :class:`AbinitInput` for performing phonon calculations.
GS input + the input files for the phonon calculation.
Args:
structure: :class:`Structure` object.
pseudos: List of filenames or list of :class:`Pseudo` objects or :class:`PseudoTable` object.
kppa: Defines the sampling used for the SCF run.
ecut: cutoff energy in Ha (if None, ecut is initialized from the pseudos according to accuracy)
pawecutdg: cutoff energy in Ha for PAW double-grid (if None, pawecutdg is initialized from the
pseudos according to accuracy)
scf_nband: Number of bands for SCF run. If scf_nband is None, nband is automatically initialized from the list of
pseudos, the structure and the smearing option.
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.
"""
# Build the input file for the GS run.
gs_inp = AbinitInput(structure=structure, pseudos=pseudos)
# Set the cutoff energies.
gs_inp.set_vars(_find_ecut_pawecutdg(ecut, pawecutdg, gs_inp.pseudos))
ksampling = aobj.KSampling.automatic_density(gs_inp.structure, kppa, chksymbreak=0)
gs_inp.set_vars(ksampling.to_abivars())
gs_inp.set_vars(tolvrs=1.0e-18)
# Get the qpoints in the IBZ. Note that here we use a q-mesh with ngkpt=(4,4,4) and shiftk=(0,0,0)
# i.e. the same parameters used for the k-mesh in gs_inp.
qpoints = gs_inp.abiget_ibz(ngkpt=(4,4,4), shiftk=(0,0,0), kptopt=1).points
#print("get_ibz qpoints:", qpoints)
# Build the input files for the q-points in the IBZ.
ph_inputs = MultiDataset(gs_inp.structure, pseudos=gs_inp.pseudos, ndtset=len(qpoints))
for ph_inp, qpt in zip(ph_inputs, qpoints):
# Response-function calculation for phonons.
ph_inp.set_vars(
rfphon=1, # Will consider phonon-type perturbation
nqpt=1, # One wavevector is to be considered
qpt=qpt, # This wavevector is q=0 (Gamma)
tolwfr=1.0e-20,
kptopt=3, # One could used symmetries for Gamma.
)
#rfatpol 1 1 # Only the first atom is displaced
#rfdir 1 0 0 # Along the first reduced coordinate axis
#kptopt 2 # Automatic generation of k points, taking
irred_perts = ph_inp.abiget_irred_phperts()
#for pert in irred_perts:
# #print(pert)
# # TODO this will work for phonons, but not for the other types of perturbations.
# ph_inp = q_inp.deepcopy()
# rfdir = 3 * [0]
# rfdir[pert.idir -1] = 1
# ph_inp.set_vars(
# rfdir=rfdir,
# rfatpol=[pert.ipert, pert.ipert]
# )
# ph_inputs.append(ph_inp)
# Split input into gs_inp and ph_inputs
all_inps = [gs_inp]
all_inps.extend(ph_inputs.split_datasets())
return all_inps
def bse_with_mdf_inputs(structure, pseudos,
scf_kppa, nscf_nband, nscf_ngkpt, nscf_shiftk,
ecuteps, bs_loband, bs_nband, soenergy, mdf_epsinf,
ecut=None, pawecutdg=None,
exc_type="TDA", bs_algo="haydock", accuracy="normal", spin_mode="polarized",
smearing="fermi_dirac:0.1 eV", charge=0.0, scf_algorithm=None):
"""
Returns a :class:`AbinitInput` object that performs a GS + NSCF + Bethe-Salpeter calculation.
The self-energy corrections are approximated with the scissors operator.
The screening in modeled with the model dielectric function.
Args:
structure: :class:`Structure` object.
pseudos: List of filenames or list of :class:`Pseudo` objects or :class:`PseudoTable` object.
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.
ecut: cutoff energy in Ha (if None, ecut is initialized from the pseudos according to accuracy)
pawecutdg: cutoff energy in Ha for PAW double-grid (if None, pawecutdg is initialized from the pseudos
according to accuracy)
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.
"""
structure = Structure.as_structure(structure)
multi = MultiDataset(structure, pseudos, ndtset=3)
# Set the cutoff energies.
d = _find_ecut_pawecutdg(ecut, pawecutdg, multi.pseudos)
multi.set_vars(ecut=d.ecut, ecutwfn=d.ecut, pawecutdg=d.pawecutdg)
# Ground-state
scf_ksampling = aobj.KSampling.automatic_density(structure, scf_kppa, chksymbreak=0)
scf_electrons = aobj.Electrons(spin_mode=spin_mode, smearing=smearing, algorithm=scf_algorithm,
charge=charge, nband=None, fband=None)
if scf_electrons.nband is None:
scf_electrons.nband = _find_scf_nband(structure, multi.pseudos, scf_electrons)
multi[0].set_vars(scf_ksampling.to_abivars())
multi[0].set_vars(scf_electrons.to_abivars())
multi[0].set_vars(_stopping_criterion("scf", accuracy))
# NSCF calculation with the randomly-shifted k-mesh.
nscf_ksampling = aobj.KSampling.monkhorst(nscf_ngkpt, shiftk=nscf_shiftk, chksymbreak=0)
nscf_electrons = aobj.Electrons(spin_mode=spin_mode, smearing=smearing, algorithm={"iscf": -2},
charge=charge, nband=nscf_nband, fband=None)
multi[1].set_vars(nscf_ksampling.to_abivars())
multi[1].set_vars(nscf_electrons.to_abivars())
multi[1].set_vars(_stopping_criterion("nscf", accuracy))
# BSE calculation.
exc_ham = aobj.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)
multi[2].set_vars(nscf_ksampling.to_abivars())
multi[2].set_vars(nscf_electrons.to_abivars())
multi[2].set_vars(exc_ham.to_abivars())
#multi[2].set_vars(_stopping_criterion("nscf", accuracy))
# TODO: Cannot use istwfk != 1.
multi.set_vars(istwfk="*1")
return multi
def g0w0_with_ppmodel_inputs(structure, pseudos,
kppa, nscf_nband, ecuteps, ecutsigx,
ecut=None, pawecutdg=None,
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):
"""
Returns a :class:`AbinitInput` object that performs G0W0 calculations with the plasmon pole approximation.
Args:
structure: Pymatgen structure.
pseudos: List of filenames or list of :class:`Pseudo` objects or :class:`PseudoTable` object.
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.
ecut: cutoff energy in Ha (if None, ecut is initialized from the pseudos according to accuracy)
pawecutdg: cutoff energy in Ha for PAW double-grid (if None, pawecutdg is initialized
from the pseudos according to accuracy)
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.
"""
structure = Structure.as_structure(structure)
multi = MultiDataset(structure, pseudos, ndtset=4)
# Set the cutoff energies.
multi.set_vars(_find_ecut_pawecutdg(ecut, pawecutdg, multi.pseudos))
scf_ksampling = aobj.KSampling.automatic_density(structure, kppa, chksymbreak=0)
scf_electrons = aobj.Electrons(spin_mode=spin_mode, smearing=smearing, algorithm=scf_algorithm,
charge=charge, nband=None, fband=None)
if scf_electrons.nband is None:
scf_electrons.nband = _find_scf_nband(structure, multi.pseudos, scf_electrons)
multi[0].set_vars(scf_ksampling.to_abivars())
multi[0].set_vars(scf_electrons.to_abivars())
multi[0].set_vars(_stopping_criterion("scf", accuracy))
nscf_ksampling = aobj.KSampling.automatic_density(structure, kppa, chksymbreak=0)
nscf_electrons = aobj.Electrons(spin_mode=spin_mode, smearing=smearing, algorithm={"iscf": -2},
charge=charge, nband=nscf_nband, fband=None)
multi[1].set_vars(nscf_ksampling.to_abivars())
multi[1].set_vars(nscf_electrons.to_abivars())
multi[1].set_vars(_stopping_criterion("nscf", accuracy))
# nbdbuf
# Screening.
if scr_nband is None: scr_nband = nscf_nband
screening = aobj.Screening(ecuteps, scr_nband, w_type="RPA", sc_mode="one_shot",
hilbert=None, ecutwfn=None, inclvkb=inclvkb)
multi[2].set_vars(nscf_ksampling.to_abivars())
multi[2].set_vars(nscf_electrons.to_abivars())
multi[2].set_vars(screening.to_abivars())
multi[2].set_vars(_stopping_criterion("screening", accuracy)) # Dummy
#scr_strategy = ScreeningStrategy(scf_strategy, nscf_strategy, screening)
# Sigma.
if sigma_nband is None: sigma_nband = nscf_nband
self_energy = aobj.SelfEnergy("gw", "one_shot", sigma_nband, ecutsigx, screening,
gw_qprange=gw_qprange, ppmodel=ppmodel)
multi[3].set_vars(nscf_ksampling.to_abivars())
multi[3].set_vars(nscf_electrons.to_abivars())
multi[3].set_vars(self_energy.to_abivars())
multi[3].set_vars(_stopping_criterion("sigma", accuracy)) # Dummy
#sigma_strategy = aobj.SelfEnergyStrategy(scf_strategy, nscf_strategy, scr_strategy, self_energy)
# TODO: Cannot use istwfk != 1.
multi.set_vars(istwfk="*1")
return multi
def ebands_input(structure, pseudos,
kppa=None, nscf_nband=None, ndivsm=15,
ecut=None, pawecutdg=None, scf_nband=None, accuracy="normal", spin_mode="polarized",
smearing="fermi_dirac:0.1 eV", charge=0.0, scf_algorithm=None, dos_kppa=None):
"""
Returns a :class:`AbinitInput` for band structure calculations.
Args:
structure: :class:`Structure` object.
pseudos: List of filenames or list of :class:`Pseudo` objects or :class:`PseudoTable` object.
kppa: Defines the sampling used for the SCF run. Defaults to 1000 if not given.
nscf_nband: Number of bands included in the NSCF run. Set to scf_nband + 10 if None.
ndivsm: Number of divisions used to sample the smallest segment of the k-path.
ecut: cutoff energy in Ha (if None, ecut is initialized from the pseudos according to accuracy)
pawecutdg: cutoff energy in Ha for PAW double-grid (if None, pawecutdg is initialized from the pseudos
according to accuracy)
scf_nband: Number of bands for SCF run. If scf_nband is None, nband is automatically initialized
from the list of pseudos, the structure and the smearing option.
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: Scalar or List of integers with the number of k-points per atom
to be used for the computation of the DOS (None if DOS is not wanted).
"""
structure = Structure.as_structure(structure)
if dos_kppa is not None and not isinstance(dos_kppa, (list, tuple)):
dos_kppa = [dos_kppa]
multi = MultiDataset(structure, pseudos, ndtset=2 if dos_kppa is None else 2 + len(dos_kppa))
# Set the cutoff energies.
multi.set_vars(_find_ecut_pawecutdg(ecut, pawecutdg, multi.pseudos))
# SCF calculation.
kppa = _DEFAULTS.get("kppa") if kppa is None else kppa
scf_ksampling = aobj.KSampling.automatic_density(structure, kppa, chksymbreak=0)
scf_electrons = aobj.Electrons(spin_mode=spin_mode, smearing=smearing, algorithm=scf_algorithm,
charge=charge, nband=scf_nband, fband=None)
if spin_mode=="polarized":
multi[0].set_autospinat()
if scf_electrons.nband is None:
scf_electrons.nband = _find_scf_nband(structure, multi.pseudos, scf_electrons, multi[0].get('spinat', None))
multi[0].set_vars(scf_ksampling.to_abivars())
multi[0].set_vars(scf_electrons.to_abivars())
multi[0].set_vars(_stopping_criterion("scf", accuracy))
# Band structure calculation.
nscf_ksampling = aobj.KSampling.path_from_structure(ndivsm, structure)
nscf_nband = scf_electrons.nband + 10 if nscf_nband is None else nscf_nband
nscf_electrons = aobj.Electrons(spin_mode=spin_mode, smearing=smearing, algorithm={"iscf": -2},
charge=charge, nband=nscf_nband, fband=None)
multi[1].set_vars(nscf_ksampling.to_abivars())
multi[1].set_vars(nscf_electrons.to_abivars())
multi[1].set_vars(_stopping_criterion("nscf", accuracy))
# DOS calculation with different values of kppa.
if dos_kppa is not None:
for i, kppa in enumerate(dos_kppa):
dos_ksampling = aobj.KSampling.automatic_density(structure, kppa, chksymbreak=0)
#dos_ksampling = aobj.KSampling.monkhorst(dos_ngkpt, shiftk=dos_shiftk, chksymbreak=0)
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))
return multi
def scf_phonons_inputs(structure,
pseudos,
kppa,
ecut=None,
pawecutdg=None,
scf_nband=None,
accuracy="normal",
spin_mode="polarized",
smearing="fermi_dirac:0.1 eV",
charge=0.0,
scf_algorithm=None):
"""
Returns a :class:`AbinitInput` for performing phonon calculations.
GS input + the input files for the phonon calculation.
Args:
structure: :class:`Structure` object.
pseudos: List of filenames or list of :class:`Pseudo` objects or :class:`PseudoTable` object.
kppa: Defines the sampling used for the SCF run.
ecut: cutoff energy in Ha (if None, ecut is initialized from the pseudos according to accuracy)
pawecutdg: cutoff energy in Ha for PAW double-grid (if None, pawecutdg is initialized from the
pseudos according to accuracy)
scf_nband: Number of bands for SCF run. If scf_nband is None, nband is automatically initialized from the list of
pseudos, the structure and the smearing option.
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.
"""
# Build the input file for the GS run.
gs_inp = AbinitInput(structure=structure, pseudos=pseudos)
# Set the cutoff energies.
gs_inp.set_vars(_find_ecut_pawecutdg(ecut, pawecutdg, gs_inp.pseudos))
ksampling = aobj.KSampling.automatic_density(gs_inp.structure,
kppa,
chksymbreak=0)
gs_inp.set_vars(ksampling.to_abivars())
gs_inp.set_vars(tolvrs=1.0e-18)
# Get the qpoints in the IBZ. Note that here we use a q-mesh with ngkpt=(4,4,4) and shiftk=(0,0,0)
# i.e. the same parameters used for the k-mesh in gs_inp.
qpoints = gs_inp.abiget_ibz(ngkpt=(4, 4, 4), shiftk=(0, 0, 0),
kptopt=1).points
#print("get_ibz qpoints:", qpoints)
# Build the input files for the q-points in the IBZ.
ph_inputs = MultiDataset(gs_inp.structure,
pseudos=gs_inp.pseudos,
ndtset=len(qpoints))
for ph_inp, qpt in zip(ph_inputs, qpoints):
# Response-function calculation for phonons.
ph_inp.set_vars(
rfphon=1, # Will consider phonon-type perturbation
nqpt=1, # One wavevector is to be considered
qpt=qpt, # This wavevector is q=0 (Gamma)
tolwfr=1.0e-20,
kptopt=3, # One could used symmetries for Gamma.
)
#rfatpol 1 1 # Only the first atom is displaced
#rfdir 1 0 0 # Along the first reduced coordinate axis
#kptopt 2 # Automatic generation of k points, taking
irred_perts = ph_inp.abiget_irred_phperts()
#for pert in irred_perts:
# #print(pert)
# # TODO this will work for phonons, but not for the other types of perturbations.
# ph_inp = q_inp.deepcopy()
# rfdir = 3 * [0]
# rfdir[pert.idir -1] = 1
# ph_inp.set_vars(
# rfdir=rfdir,
# rfatpol=[pert.ipert, pert.ipert]
# )
# ph_inputs.append(ph_inp)
# Split input into gs_inp and ph_inputs
all_inps = [gs_inp]
all_inps.extend(ph_inputs.split_datasets())
return all_inps
def bse_with_mdf_input(structure,
pseudos,
scf_kppa,
nscf_nband,
nscf_ngkpt,
nscf_shiftk,
ecuteps,
bs_loband,
bs_nband,
soenergy,
mdf_epsinf,
ecut=None,
pawecutdg=None,
exc_type="TDA",
bs_algo="haydock",
accuracy="normal",
spin_mode="polarized",
smearing="fermi_dirac:0.1 eV",
charge=0.0,
scf_algorithm=None):
"""
Returns a :class:`AbinitInput` object that performs a GS + NSCF + Bethe-Salpeter calculation.
The self-energy corrections are approximated with the scissors operator.
The screening in modeled with the model dielectric function.
Args:
structure: :class:`Structure` object.
pseudos: List of filenames or list of :class:`Pseudo` objects or :class:`PseudoTable` object.
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.
ecut: cutoff energy in Ha (if None, ecut is initialized from the pseudos according to accuracy)
pawecutdg: cutoff energy in Ha for PAW double-grid (if None, pawecutdg is initialized from the pseudos
according to accuracy)
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.
"""
structure = Structure.as_structure(structure)
multi = MultiDataset(structure, pseudos, ndtset=3)
# Set the cutoff energies.
d = _find_ecut_pawecutdg(ecut, pawecutdg, multi.pseudos)
multi.set_vars(ecut=d.ecut, ecutwfn=d.ecut, pawecutdg=d.pawecutdg)
# Ground-state
scf_ksampling = aobj.KSampling.automatic_density(structure,
scf_kppa,
chksymbreak=0)
scf_electrons = aobj.Electrons(spin_mode=spin_mode,
smearing=smearing,
algorithm=scf_algorithm,
charge=charge,
nband=None,
fband=None)
if scf_electrons.nband is None:
scf_electrons.nband = _find_scf_nband(structure, multi.pseudos,
scf_electrons)
multi[0].set_vars(scf_ksampling.to_abivars())
multi[0].set_vars(scf_electrons.to_abivars())
multi[0].set_vars(_stopping_criterion("scf", accuracy))
# NSCF calculation with the randomly-shifted k-mesh.
nscf_ksampling = aobj.KSampling.monkhorst(nscf_ngkpt,
shiftk=nscf_shiftk,
chksymbreak=0)
nscf_electrons = aobj.Electrons(spin_mode=spin_mode,
smearing=smearing,
algorithm={"iscf": -2},
charge=charge,
nband=nscf_nband,
fband=None)
multi[1].set_vars(nscf_ksampling.to_abivars())
multi[1].set_vars(nscf_electrons.to_abivars())
multi[1].set_vars(_stopping_criterion("nscf", accuracy))
# BSE calculation.
exc_ham = aobj.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)
multi[2].set_vars(nscf_ksampling.to_abivars())
multi[2].set_vars(nscf_electrons.to_abivars())
multi[2].set_vars(exc_ham.to_abivars())
#multi[2].set_vars(_stopping_criterion("nscf", accuracy))
# TODO: Cannot use istwfk != 1.
multi.set_vars(istwfk="*1")
return multi
def g0w0_with_ppmodel_input(structure,
pseudos,
kppa,
nscf_nband,
ecuteps,
ecutsigx,
ecut=None,
pawecutdg=None,
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):
"""
Returns a :class:`AbinitInput` object that performs G0W0 calculations with the plasmon pole approximation.
Args:
structure: Pymatgen structure.
pseudos: List of filenames or list of :class:`Pseudo` objects or :class:`PseudoTable` object.
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.
ecut: cutoff energy in Ha (if None, ecut is initialized from the pseudos according to accuracy)
pawecutdg: cutoff energy in Ha for PAW double-grid (if None, pawecutdg is initialized
from the pseudos according to accuracy)
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.
"""
structure = Structure.as_structure(structure)
multi = MultiDataset(structure, pseudos, ndtset=4)
# Set the cutoff energies.
multi.set_vars(_find_ecut_pawecutdg(ecut, pawecutdg, multi.pseudos))
scf_ksampling = aobj.KSampling.automatic_density(structure,
kppa,
chksymbreak=0)
scf_electrons = aobj.Electrons(spin_mode=spin_mode,
smearing=smearing,
algorithm=scf_algorithm,
charge=charge,
nband=None,
fband=None)
if scf_electrons.nband is None:
scf_electrons.nband = _find_scf_nband(structure, multi.pseudos,
scf_electrons)
multi[0].set_vars(scf_ksampling.to_abivars())
multi[0].set_vars(scf_electrons.to_abivars())
multi[0].set_vars(_stopping_criterion("scf", accuracy))
nscf_ksampling = aobj.KSampling.automatic_density(structure,
kppa,
chksymbreak=0)
nscf_electrons = aobj.Electrons(spin_mode=spin_mode,
smearing=smearing,
algorithm={"iscf": -2},
charge=charge,
nband=nscf_nband,
fband=None)
multi[1].set_vars(nscf_ksampling.to_abivars())
multi[1].set_vars(nscf_electrons.to_abivars())
multi[1].set_vars(_stopping_criterion("nscf", accuracy))
# nbdbuf
# Screening.
if scr_nband is None: scr_nband = nscf_nband
screening = aobj.Screening(ecuteps,
scr_nband,
w_type="RPA",
sc_mode="one_shot",
hilbert=None,
ecutwfn=None,
inclvkb=inclvkb)
multi[2].set_vars(nscf_ksampling.to_abivars())
multi[2].set_vars(nscf_electrons.to_abivars())
multi[2].set_vars(screening.to_abivars())
multi[2].set_vars(_stopping_criterion("screening", accuracy)) # Dummy
#scr_strategy = ScreeningStrategy(scf_strategy, nscf_strategy, screening)
# Sigma.
if sigma_nband is None: sigma_nband = nscf_nband
self_energy = aobj.SelfEnergy("gw",
"one_shot",
sigma_nband,
ecutsigx,
screening,
gw_qprange=gw_qprange,
ppmodel=ppmodel)
multi[3].set_vars(nscf_ksampling.to_abivars())
multi[3].set_vars(nscf_electrons.to_abivars())
multi[3].set_vars(self_energy.to_abivars())
multi[3].set_vars(_stopping_criterion("sigma", accuracy)) # Dummy
#sigma_strategy = aobj.SelfEnergyStrategy(scf_strategy, nscf_strategy, scr_strategy, self_energy)
# TODO: Cannot use istwfk != 1.
multi.set_vars(istwfk="*1")
return multi
def ebands_input(structure,
pseudos,
kppa=None,
nscf_nband=None,
ndivsm=15,
ecut=None,
pawecutdg=None,
scf_nband=None,
accuracy="normal",
spin_mode="polarized",
smearing="fermi_dirac:0.1 eV",
charge=0.0,
scf_algorithm=None,
dos_kppa=None):
"""
Returns a :class:`AbinitInput` for band structure calculations.
Args:
structure: :class:`Structure` object.
pseudos: List of filenames or list of :class:`Pseudo` objects or :class:`PseudoTable` object.
kppa: Defines the sampling used for the SCF run. Defaults to 1000 if not given.
nscf_nband: Number of bands included in the NSCF run. Set to scf_nband + 10 if None.
ndivsm: Number of divisions used to sample the smallest segment of the k-path.
ecut: cutoff energy in Ha (if None, ecut is initialized from the pseudos according to accuracy)
pawecutdg: cutoff energy in Ha for PAW double-grid (if None, pawecutdg is initialized from the pseudos
according to accuracy)
scf_nband: Number of bands for SCF run. If scf_nband is None, nband is automatically initialized
from the list of pseudos, the structure and the smearing option.
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: Scalar or List of integers with the number of k-points per atom
to be used for the computation of the DOS (None if DOS is not wanted).
"""
structure = Structure.as_structure(structure)
if dos_kppa is not None and not isinstance(dos_kppa, (list, tuple)):
dos_kppa = [dos_kppa]
multi = MultiDataset(structure,
pseudos,
ndtset=2 if dos_kppa is None else 2 + len(dos_kppa))
# Set the cutoff energies.
multi.set_vars(_find_ecut_pawecutdg(ecut, pawecutdg, multi.pseudos))
# SCF calculation.
kppa = _DEFAULTS.get("kppa") if kppa is None else kppa
scf_ksampling = aobj.KSampling.automatic_density(structure,
kppa,
chksymbreak=0)
scf_electrons = aobj.Electrons(spin_mode=spin_mode,
smearing=smearing,
algorithm=scf_algorithm,
charge=charge,
nband=scf_nband,
fband=None)
if scf_electrons.nband is None:
scf_electrons.nband = _find_scf_nband(structure, multi.pseudos,
scf_electrons)
multi[0].set_vars(scf_ksampling.to_abivars())
multi[0].set_vars(scf_electrons.to_abivars())
multi[0].set_vars(_stopping_criterion("scf", accuracy))
# Band structure calculation.
nscf_ksampling = aobj.KSampling.path_from_structure(ndivsm, structure)
nscf_nband = scf_electrons.nband + 10 if nscf_nband is None else nscf_nband
nscf_electrons = aobj.Electrons(spin_mode=spin_mode,
smearing=smearing,
algorithm={"iscf": -2},
charge=charge,
nband=nscf_nband,
fband=None)
multi[1].set_vars(nscf_ksampling.to_abivars())
multi[1].set_vars(nscf_electrons.to_abivars())
multi[1].set_vars(_stopping_criterion("nscf", accuracy))
# DOS calculation with different values of kppa.
if dos_kppa is not None:
for i, kppa in enumerate(dos_kppa):
dos_ksampling = aobj.KSampling.automatic_density(structure,
kppa,
chksymbreak=0)
#dos_ksampling = aobj.KSampling.monkhorst(dos_ngkpt, shiftk=dos_shiftk, chksymbreak=0)
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))
return multi
