def itest_phonon_restart(fwp):
"""Test the restart of phonon calculations with the scheduler."""
# Crystalline AlAs: computation of the second derivative of the total energy
structure = abidata.structure_from_ucell("AlAs")
# List of q-points for the phonon calculation (4,4,4) mesh.
qpoints = np.reshape([
0.00000000E+00, 0.00000000E+00, 0.00000000E+00,
2.50000000E-01, 0.00000000E+00, 0.00000000E+00,
#5.00000000E-01, 0.00000000E+00, 0.00000000E+00, # XXX Uncomment this line to test restart from 1DEN
# Too long --> disabled
], (-1, 3))
# Global variables used both for the GS and the DFPT run.
global_vars = dict(nband=4,
ecut=3.0,
ngkpt=[4, 4, 4],
shiftk=[0, 0, 0],
tolvrs=1.0e-5,
)
multi = abilab.MultiDataset(structure=structure, pseudos=abidata.pseudos("13al.981214.fhi", "33as.pspnc"),
ndtset=1+len(qpoints))
multi.set_vars(global_vars)
for i, qpt in enumerate(qpoints):
# Response-function calculation for phonons.
multi[i+1].set_vars(
rfphon=1, # Will consider phonon-type perturbation.
nqpt=1, # One wavevector is to be considered.
qpt=qpt, # q-wavevector.
kptopt=3,
nstep=5, # This is to trigger the phonon restart.
)
#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
# i == 0 --> restart from WFK
if i == 1: multi[i+1].set_vars(prtwf=-1, nstep=5) # Restart with WFK and smart- io.
if i == 2: multi[i+1].set_vars(prtwf=0, nstep=8) # Restart from 1DEN. Too long --> disabled.
all_inps = multi.split_datasets()
scf_input, ph_inputs = all_inps[0], all_inps[1:]
flow = abilab.phonon_flow(fwp.workdir, scf_input, ph_inputs, manager=fwp.manager)
flow.set_garbage_collector()
for task in flow.iflat_tasks():
task.manager.qadapter.max_num_launches = 20
assert flow.make_scheduler().start() == 0
flow.check_status(show=True, verbose=1)
assert all(work.finalized for work in flow)
assert flow.all_ok
assert sum(task.num_restarts for task in flow.iflat_tasks()) > 0
def build_flow(options):
# Set 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("GaAs")
pseudos = abidata.pseudos("Ga-low_r.psp8", "As_r.psp8")
num_electrons = structure.num_valence_electrons(pseudos)
#print("num_electrons:", num_electrons)
# Usa same shifts in all tasks.
ngkpt = [4, 4, 4]
shiftk = [
[0.5, 0.5, 0.5],
[0.5, 0.0, 0.0],
[0.0, 0.5, 0.0],
[0.0, 0.0, 0.5],
]
# NSCF run on k-path with large number of bands
kptbounds = [
[0.5, 0.0, 0.0], # L point
[0.0, 0.0, 0.0], # Gamma point
[0.0, 0.5, 0.5], # X point
]
# Initialize the flow.
flow = flowtk.Flow(options.workdir, manager=options.manager)
for nspinor in [1, 2]:
# Multi will contain two datasets (GS + NSCF) for the given nspinor.
multi = abilab.MultiDataset(structure=structure, pseudos=pseudos, ndtset=2)
# Global variables.
multi.set_vars(
ecut=20,
nspinor=nspinor,
nspden=1 if nspinor == 1 else 4,
so_psp="*0" if nspinor == 1 else "*1", # Important!
#paral_kgb=1,
)
nband_occ = num_electrons // 2 if nspinor == 1 else num_electrons
#print(nband_occ)
# Dataset 1 (GS run)
multi[0].set_vars(tolvrs=1e-8, nband=nband_occ + 4)
multi[0].set_kmesh(ngkpt=ngkpt, shiftk=shiftk, kptopt=1 if nspinor == 1 else 4)
multi[1].set_vars(iscf=-2, nband=nband_occ + 4, tolwfr=1.e-12)
multi[1].set_kpath(ndivsm=10, kptbounds=kptbounds)
# Get the SCF and the NSCF input.
scf_input, nscf_input = multi.split_datasets()
flow.register_work(flowtk.BandStructureWork(scf_input, nscf_input))
return flow
def make_inputs(paw=False):
pseudos = abidata.pseudos("14si.pspnc", "8o.pspnc") if not paw else \
abidata.pseudos("Si.GGA_PBE-JTH-paw.xml", "o.paw")
structure = abidata.structure_from_ucell("SiO2-alpha")
multi = abilab.MultiDataset(structure, pseudos=pseudos, ndtset=4)
ecut = 24
multi.set_vars(
ecut=ecut,
pawecutdg=ecut * 2 if paw else None,
paral_kgb=0,
istwfk="*1",
timopt=-1,
)
multi.set_kmesh(ngkpt=[4, 4, 3], shiftk=[0.0, 0.0, 0.0])
gs, nscf, scr, sigma = multi.split_datasets()
# Dataset 1 (GS run)
gs.set_vars(
tolvrs=1e-6,
nband=28,
)
# Dataset 2 (NSCF run)
nscf.set_vars(
iscf=-2,
tolwfr=1e-4,
nband=600,
nbdbuf=200,
)
# Dataset3: Calculation of the screening.
scr.set_vars(
optdriver=3,
gwpara=2,
nband=25,
ecutwfn=ecut,
symchi=1,
inclvkb=0,
ecuteps=6.0,
)
# Dataset4: Calculation of the Self-Energy matrix elements (GW corrections)
sigma.set_vars(
optdriver=4,
gwpara=2,
ecutwfn=ecut,
ecuteps=6.0,
ecutsigx=ecut,
symsigma=1,
gw_qprange=1,
)
return gs, nscf, scr, sigma
def make_inputs(paw=False):
# Crystalline silicon
# Calculation of the GW correction to the direct band gap in Gamma
# Dataset 1: ground state calculation
# Dataset 2: BSE with haydock method and model dielectric function.
structure = abilab.Structure.from_file(abidata.cif_file("si.cif"))
pseudos = abidata.pseudos("14si.pspnc") if not paw else abidata.pseudos(
"Si.GGA_PBE-JTH-paw.xml")
multi = abilab.MultiDataset(structure, pseudos=pseudos, ndtset=2)
ecut = 8
multi.set_vars(
ecut=ecut,
pawecutdg=ecut * 4 if paw else None,
nsppol=1,
timopt=-1,
istwfk="*1",
paral_kgb=0,
)
# This grid is the most economical, but does not contain the Gamma point.
multi.set_kmesh(
ngkpt=[8, 8, 8],
shiftk=[0.0, 0.0, 0.0],
)
gs, bse = multi.split_datasets()
gs.set_vars(
tolvrs=1e-6,
nband=8,
)
# Dataset 6 BSE equation with Model dielectric function and Haydock (only resonant + W + v)
# Note that SCR file is not needed here
bse.set_vars(
optdriver=99,
ecutwfn=ecut,
ecuteps=4.0,
inclvkb=2,
bs_algorithm=2, # Haydock
bs_haydock_niter=60, # No. of iterations for Haydock
bs_exchange_term=1,
bs_coulomb_term=21, # Use model W and full W_GG.
mdf_epsinf=12.0,
bs_calctype=1, # Use KS energies and orbitals to construct L0
mbpt_sciss="0.8 eV",
bs_coupling=0,
bs_loband=2,
nband=8,
bs_freq_mesh="0 10 0.1 eV",
bs_hayd_term=0, # No terminator
#gwmem=01 # Compute the model-dielectric function on-the-fly.
)
return gs, bse
def make_scf_nscf_dos_inputs(structure, pseudos, luj_params, paral_kgb=1):
# Input file taken from tldau_2.in
multi = abilab.MultiDataset(structure, pseudos=pseudos, ndtset=3)
# Global variables
global_vars = dict(
#
ecut=12,
pawecutdg=30,
nband=40,
occopt=7,
tsmear=0.015,
nstep=50,
paral_kgb=paral_kgb,
#
# Spin
nsppol=1,
nspden=2,
nspinor=1,
spinat=[0, 0, 1,
0, 0, -1,
0, 0, 0,
0, 0, 0],
# Kpoint Grid
# The k point grid is not symmetric, but the calculations being
# for the ground-state, this is not a problem.
)
multi.set_vars(global_vars)
multi.set_vars(luj_params.to_abivars())
# GS run.
multi[0].set_vars(
iscf=17,
toldfe=1.0e-8,
ngkpt=[2, 2, 2],
chksymbreak=0,
)
# Band structure run.
multi[1].set_kpath(ndivsm=6)
multi[1].set_vars(tolwfr=1e-10)
# Dos calculation.
multi[2].set_vars(
iscf=-3, # NSCF calculation
ngkpt=structure.calc_ngkpt(nksmall=8),
shiftk=[0.0, 0.0, 0.0],
nshiftk=1,
tolwfr=1.e-8,
#pawprtdos=1,
)
# Generate two input files for the GS and the NSCF run
scf_input, nscf_input, dos_input = multi.split_datasets()
return scf_input, nscf_input, dos_input
def make_inputs(tvars):
"""Constrcut the input files."""
structure = abidata.structure_from_ucell("GaAs")
multi = abilab.MultiDataset(structure, pseudos=abidata.pseudos("31ga.pspnc", "33as.pspnc"), ndtset=5)
# Global variables
kmesh = dict(ngkpt=[4, 4, 4],
nshiftk=4,
shiftk=[[0.5, 0.5, 0.5],
[0.5, 0.0, 0.0],
[0.0, 0.5, 0.0],
[0.0, 0.0, 0.5]])
global_vars = dict(ecut=2, paral_kgb=tvars.paral_kgb)
global_vars.update(kmesh)
multi.set_vars(global_vars)
# Dataset 1 (GS run)
multi[0].set_vars(
tolvrs=1e-6,
nband=4)
# NSCF run with large number of bands, and points in the the full BZ
multi[1].set_vars(
iscf=-2,
nband=20,
nstep=25,
kptopt=1,
tolwfr=1.e-8)
#kptopt=3)
# Fourth dataset: ddk response function along axis 1
# Fifth dataset: ddk response function along axis 2
# Sixth dataset: ddk response function along axis 3
for idir in range(3):
rfdir = 3 * [0]
rfdir[idir] = 1
multi[2+idir].set_vars(
iscf=-3,
nband=20,
nstep=1,
nline=0,
prtwf=3,
kptopt=3,
nqpt=1,
qpt=[0.0, 0.0, 0.0],
rfdir=rfdir,
rfelfd=2,
tolwfr=1.e-9,
)
# scf_inp, nscf_inp, ddk1, ddk2, ddk3
return multi.split_datasets()
def make_inputs(paral_kgb=1):
"""
Returns a tuple of 4 input files for SCF, NSCF, SCR, SIGMA calculations.
These files are then used as templates for the convergence study
wrt ecuteps and the number of bands in W.
"""
multi = abilab.MultiDataset(abidata.structure_from_ucell("SiC"),
pseudos=abidata.pseudos(
"14si.pspnc", "6c.pspnc"),
ndtset=4)
ecut = 12
global_vars = dict(ecut=ecut, istwfk="*1", paral_kgb=paral_kgb, gwpara=2)
ecuteps = 4
ngkpt = [4, 4, 4]
shiftk = [0, 0, 0]
multi.set_vars(global_vars)
multi.set_kmesh(ngkpt=ngkpt, shiftk=shiftk)
# SCF
multi[0].set_vars(
nband=10,
tolvrs=1.e-8,
)
# NSCF
multi[1].set_vars(nband=25, tolwfr=1.e-8, iscf=-2)
# SCR
multi[2].set_vars(
optdriver=3,
ecutwfn=ecut,
nband=20,
symchi=1,
inclvkb=0,
ecuteps=ecuteps,
)
# SIGMA
multi[3].set_vars(
optdriver=4,
nband=20,
ecutwfn=ecut,
ecutsigx=ecut,
#ecutsigx=(4*ecut), ! This is problematic
symsigma=1,
ecuteps=ecuteps,
)
multi[3].set_kptgw(kptgw=[[0, 0, 0], [0.5, 0, 0]], bdgw=[1, 8])
return multi.split_datasets()
def make_ion_ioncell_inputs(paral_kgb=0):
structure = abilab.Structure.from_file(abidata.cif_file("si.cif"))
# Perturb the structure (random perturbation of 0.1 Angstrom)
# then compress the volume to trigger dilatmx.
structure.perturb(distance=0.1)
structure.scale_lattice(structure.volume * 0.6)
global_vars = dict(
ecut=4,
ngkpt=[4, 4, 4],
shiftk=[0, 0, 0],
nshiftk=1,
chksymbreak=0,
paral_kgb=paral_kgb,
iomode=3,
#prtwf=0,
)
multi = abilab.MultiDataset(structure,
pseudos=abidata.pseudos("14si.pspnc"),
ndtset=2)
# Global variables
multi.set_vars(global_vars)
# Dataset 1 (Atom Relaxation)
multi[0].set_vars(
optcell=0,
ionmov=2,
tolrff=0.02,
tolmxf=5.0e-5,
#ntime=50,
ntime=3, #To test the restart
#dilatmx=1.1, # FIXME: abinit crashes if I don't use this
)
# Dataset 2 (Atom + Cell Relaxation)
multi[1].set_vars(
optcell=1,
ionmov=2,
ecutsm=0.5,
dilatmx=1.02,
tolrff=0.02,
tolmxf=5.0e-5,
strfact=100,
#ntime=50,
ntime=3, # To test the restart
)
ion_inp, ioncell_inp = multi.split_datasets()
return ion_inp, ioncell_inp
def raman_workflow(structure, pseudos, scf_t, ksamp):
# Generate 3 different input files for computing optical properties with BSE.
inp = abilab.MultiDataset(structure, pseudos=pseudos, ndtset=2)
inp.set_vars(**global_vars)
vars_ksamp = ksamp.to_abivars()
vars_ksamp.pop("#comment", None)
inp.set_vars(**vars_ksamp)
# NSCF run
inp[0].set_vars(
iscf=-2,
nband=10 * len(structure),
nbdbuf=2 * len(structure),
nstep=500,
tolwfr=1.e-22,
)
inp[1].set_vars(
gwmem=10,
gwpara=2,
optdriver=99,
nband=5 * len(structure), # 10 bands for 2 atoms
bs_loband=1 * len(structure), # start at 2 for 2 atoms
bs_algorithm=2, # Haydock
bs_calctype=1, # KS wavefunctions
bs_coulomb_term=21, # Use model dielectric function
mdf_epsinf=12.0,
bs_exchange_term=1, # Exchange term included
bs_freq_mesh="0 10 0.01 eV",
bs_hayd_term=0,
bs_coupling=0,
bs_haydock_tol="-0.01 0",
bs_haydock_niter=1000,
inclvkb=2,
ecuteps=4,
soenergy="0.8 eV",
ecutwfn=global_vars["ecut"],
)
nscf_inp, bse_inp = inp.split_datasets()
workflow = abilab.Work()
nscf_t = workflow.register(nscf_inp,
deps={scf_t: "DEN"},
task_class=abilab.NscfTask)
bse_t = workflow.register_bse_task(bse_inp, deps={nscf_t: "WFK"})
return workflow
def scf_ph_inputs(tvars=None):
"""
This function constructs the input files for the phonon calculation:
GS input + the input files for the phonon calculation.
"""
# Crystalline AlAs: computation of the second derivative of the total energy
structure = abidata.structure_from_ucell("AlAs")
# List of q-points for the phonon calculation (4,4,4) mesh.
qpoints = [
0.00000000E+00, 0.00000000E+00, 0.00000000E+00,
2.50000000E-01, 0.00000000E+00, 0.00000000E+00,
5.00000000E-01, 0.00000000E+00, 0.00000000E+00,
2.50000000E-01, 2.50000000E-01, 0.00000000E+00,
5.00000000E-01, 2.50000000E-01, 0.00000000E+00,
-2.50000000E-01, 2.50000000E-01, 0.00000000E+00,
5.00000000E-01, 5.00000000E-01, 0.00000000E+00,
-2.50000000E-01, 5.00000000E-01, 2.50000000E-01,
]
qpoints = np.reshape(qpoints, (-1,3))
# Global variables used both for the GS and the DFPT run.
global_vars = dict(nband=4,
ecut=3.0,
ngkpt=[4, 4, 4],
shiftk=[0, 0, 0],
tolvrs=1.0e-6,
paral_kgb=0 if tvars is None else tvars.paral_kgb,
)
multi = abilab.MultiDataset(structure=structure, pseudos=abidata.pseudos("13al.981214.fhi", "33as.pspnc"),
ndtset=1+len(qpoints))
multi.set_vars(global_vars)
for i, qpt in enumerate(qpoints):
# Response-function calculation for phonons.
multi[i+1].set_vars(
nstep=20,
rfphon=1, # Will consider phonon-type perturbation
nqpt=1, # One wavevector is to be considered
qpt=qpt, # This wavevector is q=0 (Gamma)
kptopt=3,
)
#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
# Split input into gs_inp and ph_inputs
return multi.split_datasets()
def itest_metagga_ebands_flow(fwp, tvars):
"""
Test band structure calculation with meta-GGA
"""
if not fwp.abinit_build.has_libxc:
pytest.skip(
"itest_metagga_ebands_flow requires libxc support in Abinit.")
from abipy.data.hgh_pseudos import HGH_TABLE
multi = abilab.MultiDataset(structure=abidata.cif_file("si.cif"),
pseudos=HGH_TABLE,
ndtset=2)
# Global variables
shiftk = [
float(s)
for s in "0.5 0.5 0.5 0.5 0.0 0.0 0.0 0.5 0.0 0.0 0.0 0.5".split()
]
multi.set_vars(ecut=20,
diemac=12,
iomode=3,
ixc=-208012,
prtkden=1,
usekden=1)
# Dataset 1
multi[0].set_vars(tolvrs=1e-7)
multi[0].set_kmesh(ngkpt=[2, 2, 2], shiftk=shiftk)
# Dataset 2
multi[1].set_vars(tolwfr=1e-8)
multi[1].set_kpath(ndivsm=2)
scf_input, nscf_input = multi.split_datasets()
work = flowtk.works.BandStructureWork(scf_input=scf_input,
nscf_input=nscf_input)
flow = abilab.Flow(workdir=fwp.workdir, manager=fwp.manager)
flow.register_work(work)
flow.build_and_pickle_dump(abivalidate=True)
fwp.scheduler.add_flow(flow)
assert fwp.scheduler.start() == 0
assert not fwp.scheduler.exceptions
#assert fwp.scheduler.nlaunch == 3
flow.show_status()
if not flow.all_ok:
flow.debug()
raise RuntimeError()
assert all(work.finalized for work in flow)
def make_ngkpt_flow():
ngkpt_list = [(2, 2, 2), (4, 4, 4), (6, 6, 6), (8, 8, 8)]
multi = abilab.MultiDataset(structure=abidata.cif_file("si.cif"),
pseudos=abidata.pseudos("14si.pspnc"),
ndtset=len(ngkpt_list))
# Global variables
multi.set_vars(ecut=10, tolvrs=1e-9)
for i, ngkpt in enumerate(ngkpt_list):
multi[i].set_kmesh(ngkpt=ngkpt, shiftk=[0, 0, 0])
return NgkptFlow.from_inputs(workdir="flow_base3_ngkpt",
inputs=multi.split_datasets())
def build_flow(options):
# Working directory (default is the name of the script with '.py' removed and "run_" replaced by "flow_")
workdir = options.workdir
if not options.workdir:
workdir = os.path.basename(__file__).replace(".py", "").replace(
"run_", "flow_")
flow = flowtk.Flow(workdir, manager=options.manager, remove=options.remove)
pseudos = data.pseudos("14si.pspnc", "6c.pspnc")
structure = data.structure_from_ucell("SiC")
global_vars = dict(
chksymbreak=0,
ecut=20,
paral_kgb=0,
iomode=3,
)
ngkpt = [4, 4, 4]
shiftk = [[0.5, 0.5, 0.5], [0.5, 0.0, 0.0], [0.0, 0.5, 0.0],
[0.0, 0.0, 0.5]]
multi = abilab.MultiDataset(structure, pseudos=pseudos, ndtset=2)
multi.set_vars(global_vars)
relax_inp, nscf_inp = multi.split_datasets()
relax_inp.set_kmesh(ngkpt=ngkpt, shiftk=shiftk)
relax_inp.set_vars(
toldff=1e-6,
tolmxf=1e-5,
strfact=100,
ecutsm=0.5,
dilatmx=1.15,
ntime=100,
ionmov=2,
optcell=1,
)
nscf_inp.set_kpath(ndivsm=20)
nscf_inp.tolwfr = 1e-22
# Initialize the work.
relax_task = flow.register_task(relax_inp, task_class=flowtk.RelaxTask)
#work = RelaxWork(self, ion_input, ioncell_input, workdir=None, manager=None):
#nscf_task = flow.register_task(nscf_inp, deps={relax_task: "DEN"}, task_class=flowtk.NscfTask)
return flow
def build_flow(options):
"""
Build and return a flow performing structural relaxations with different k-point samplings.
"""
# Set working directory (default is the name of the script with '.py' removed and "run_" replaced by "flow_")
if not options.workdir:
if os.getenv("READTHEDOCS", False):
__file__ = os.path.join(os.getcwd(), "run_relax_vs_kpts.py")
options.workdir = os.path.basename(__file__).replace(
".py", "").replace("run_", "flow_")
# List of k-meshes.
ngkpt_list = [
[3, 3, 2],
[6, 6, 4],
[8, 8, 6],
]
structure = abilab.Structure.from_file(abidata.cif_file("gan2.cif"))
pseudos = abidata.pseudos("Ga.oncvpsp", "N.oncvpsp")
# Build multidataset.
multi = abilab.MultiDataset(structure=structure,
pseudos=pseudos,
ndtset=len(ngkpt_list))
# Set global variables for structural relaxation. Note dilatmx and ecutsm
# Ecut should depend on pseudos.
multi.set_vars(
ecut=15, # Too low
optcell=2,
ionmov=3,
tolrff=5.0e-2,
tolmxf=5.0e-5,
ntime=50,
dilatmx=1.05, # Important!
ecutsm=0.5, # Important!
)
# Here we set the k-meshes (Gamma-centered for simplicity)
for i, ngkpt in enumerate(ngkpt_list):
multi[i].set_kmesh(ngkpt=ngkpt, shiftk=[0, 0, 0])
# As the calculations are independent, we can use Flow.from_inputs
# and call split_datasets to create len(ngkpt_list) inputs.
# Note that it's a good idea to specify the task_class so that AbiPy knows how to restart the calculation.
return flowtk.Flow.from_inputs(options.workdir,
inputs=multi.split_datasets(),
task_class=flowtk.RelaxTask)
def make_ngkpt_flow(ngkpt_list=((2, 2, 2), (4, 4, 4), (6, 6, 6), (8, 8, 8)),
structure_file=None,
metal=False):
"""
A `factory function` (a function that returns an instance of the class defined above. If no specific system is
specified, structure_file=None, an flow for silicon in constructed and returned.
"""
# Defining the structure and adding the appropriate pseudo potentials
if structure_file is None:
multi = abilab.MultiDataset(structure=abidata.cif_file("si.cif"),
pseudos=abidata.pseudos("14si.pspnc"),
ndtset=len(ngkpt_list))
workdir = "flow_lesson_Si_kpoint_convergence"
else:
structure = abilab.Structure.from_file(structure_file)
pseudos = get_pseudos(structure)
multi = abilab.MultiDataset(structure,
pseudos=pseudos,
ndtset=len(ngkpt_list))
workdir = "flow_lesson_" + structure.composition.reduced_formula + "_kpoint_convergence"
# Add mnemonics to input file.
multi.set_mnemonics(True)
# Global variables
multi.set_vars(ecut=10, tolvrs=1e-9)
if metal:
multi.set_vars(occopt=7, tsmear=0.04)
# Specific variables for the different calculations
for i, ngkpt in enumerate(ngkpt_list):
multi[i].set_kmesh(ngkpt=ngkpt, shiftk=[0, 0, 0])
return abilab.Flow.from_inputs(workdir=workdir,
inputs=multi.split_datasets())
def make_ecut_flow(structure_file=None, ecut_list=(10, 12, 14, 16, 18)):
"""
Build and return a `Flow` to perform a convergence study wrt to ecut.
Args:
structure_file: (optional) file containing the crystalline structure.
If None, crystalline silicon structure.
ecut_list: List of cutoff energies to be investigated.
"""
# Define the structure and add the necessary pseudos:
if structure_file is None:
multi = abilab.MultiDataset(structure=abidata.cif_file("si.cif"),
pseudos=abidata.pseudos("14si.pspnc"),
ndtset=len(ecut_list))
workdir = "flow_Si_ecut_convergence"
else:
structure = abilab.Structure.from_file(structure_file)
pseudos = abilab.PseudoTable() ## todo fix this
multi = abilab.MultiDataset(structure,
pseudos=pseudos,
ndtset=len(ecut_list))
workdir = "flow_" + structure.composition.reduced_formula + "_ecut_convergence"
# Add mnemonics to the input files.
multi.set_mnemonics(True)
# Global variables
multi.set_vars(tolvrs=1e-9)
multi.set_kmesh(ngkpt=[4, 4, 4], shiftk=[0, 0, 0])
# Here we set the value of ecut used by the i-th task.
for i, ecut in enumerate(ecut_list):
multi[i].set_vars(ecut=ecut)
return abilab.Flow.from_inputs(workdir=workdir,
inputs=multi.split_datasets())
def make_ion_ioncell_inputs(tvars, dilatmx, scalevol=1, ntime=50):
structure = abilab.Structure.from_file(abidata.cif_file("si.cif"))
# Perturb the structure (random perturbation of 0.1 Angstrom)
#structure.perturb(distance=0.01)
# Compress the lattice so that ABINIT complains about dilatmx
structure.scale_lattice(structure.volume * scalevol)
global_vars = dict(
ecut=6,
ecutsm=0.5,
ngkpt=[4, 4, 4],
shiftk=[0, 0, 0],
nshiftk=1,
chksymbreak=0,
paral_kgb=tvars.paral_kgb,
)
multi = abilab.MultiDataset(structure,
pseudos=abidata.pseudos("14si.pspnc"),
ndtset=2)
# Global variables
multi.set_vars(global_vars)
# Dataset 1 (Atom Relaxation)
multi[0].set_vars(
optcell=0,
ionmov=2,
tolrff=0.02,
tolmxf=5.0e-5,
ntime=ntime,
)
# Dataset 2 (Atom + Cell Relaxation)
multi[1].set_vars(
optcell=1,
ionmov=2,
dilatmx=dilatmx,
tolrff=0.02,
tolmxf=5.0e-5,
strfact=100,
ntime=ntime,
)
ion_inp, ioncell_inp = multi.split_datasets()
return ion_inp, ioncell_inp
def make_scf_nscf_inputs(tvars, pp_paths, nstep=50):
"""
Returns two input files: GS run and NSCF on a high symmetry k-mesh
"""
multi = abilab.MultiDataset(structure=abidata.cif_file("si.cif"),
pseudos=abidata.pseudos(pp_paths),
ndtset=2)
nval = multi[0].num_valence_electrons
assert all(inp.num_valence_electrons == 8 for inp in multi)
# Global variables
ecut = 4
global_vars = dict(
ecut=ecut,
nband=int(nval / 2),
nstep=nstep,
paral_kgb=tvars.paral_kgb,
timopt=-1,
)
if multi.ispaw:
global_vars.update(pawecutdg=2 * ecut)
multi.set_vars(global_vars)
# Dataset 1 (GS run)
multi[0].set_kmesh(ngkpt=[4, 4, 4], shiftk=[0, 0, 0])
#multi[0].set_vars(prtden=1, prtpot=1, prtvha=1, prtvxc=1, prtvhxc=1)
multi[0].set_vars(tolvrs=1e-4)
# Dataset 2 (NSCF run)
kptbounds = [
[0.5, 0.0, 0.0], # L point
[0.0, 0.0, 0.0], # Gamma point
[0.0, 0.5, 0.5], # X point
]
multi[1].set_kpath(ndivsm=2, kptbounds=kptbounds)
multi[1].set_vars(tolwfr=1e-6)
# Generate two input files for the GS and the NSCF run.
scf_input, nscf_input = multi.split_datasets()
return scf_input, nscf_input
def make_scf_nscf_inputs(paral_kgb=1):
"""Returns two input files: GS run and NSCF on a high symmetry k-mesh."""
pseudos = abidata.pseudos("14si.pspnc")
#pseudos = data.pseudos("Si.GGA_PBE-JTH-paw.xml")
multi = abilab.MultiDataset(structure=abidata.cif_file("si.cif"),
pseudos=pseudos,
ndtset=2)
multi.set_mnemonics(True)
# Global variables
ecut = 6
global_vars = dict(
ecut=ecut,
nband=8,
timopt=-1,
istwfk="*1",
nstep=15,
paral_kgb=paral_kgb,
iomode=3,
)
if multi.ispaw:
global_vars.update(pawecutdg=2 * ecut)
multi.set_vars(global_vars)
# Dataset 1 (GS run)
multi[0].set_kmesh(ngkpt=[8, 8, 8], shiftk=[0, 0, 0])
multi[0].set_vars(tolvrs=1e-6)
# Dataset 2 (NSCF run)
kptbounds = [
[0.5, 0.0, 0.0], # L point
[0.0, 0.0, 0.0], # Gamma point
[0.0, 0.5, 0.5], # X point
]
multi[1].set_kpath(ndivsm=6, kptbounds=kptbounds)
multi[1].set_vars(tolwfr=1e-12)
# Generate two input files for the GS and the NSCF run
scf_input, nscf_input = multi.split_datasets()
return scf_input, nscf_input
def make_ebands_flow():
"""Band structure calculation."""
multi = abilab.MultiDataset(structure=abidata.cif_file("si.cif"),
pseudos=abidata.pseudos("14si.pspnc"),
ndtset=2)
# Global variables
multi.set_vars(ecut=10)
# Dataset 1
multi[0].set_vars(tolvrs=1e-9)
multi[0].set_kmesh(ngkpt=[4, 4, 4], shiftk=[0, 0, 0])
# Dataset 2
multi[1].set_vars(tolwfr=1e-15)
multi[1].set_kpath(ndivsm=5)
scf_input, nscf_input = multi.split_datasets()
return flowtk.bandstructure_flow(workdir="flow_base3_ebands",
scf_input=scf_input,
nscf_input=nscf_input)
def build_relax_flow(options):
"""
Crystalline silicon: computation of the optimal lattice parameter.
Convergence with respect to the number of k points. Similar to tbase3_4.in
"""
# Structural relaxation for different k-point samplings.
ngkpt_list = [(2, 2, 2), (4, 4, 4)]
shiftk = [
float(s)
for s in "0.5 0.5 0.5 0.5 0.0 0.0 0.0 0.5 0.0 0.0 0.0 0.5".split()
]
multi = abilab.MultiDataset(structure=abidata.cif_file("si.cif"),
pseudos=abidata.pseudos("14si.pspnc"),
ndtset=len(ngkpt_list))
# Global variables
multi.set_vars(
ecut=8,
tolvrs=1e-9,
optcell=1,
ionmov=3,
ntime=10,
dilatmx=1.05,
ecutsm=0.5,
diemac=12,
iomode=3,
)
for i, ngkpt in enumerate(ngkpt_list):
multi[i].set_kmesh(ngkpt=ngkpt, shiftk=shiftk)
workdir = options.workdir if (options
and options.workdir) else "flow_base3_relax"
return flowtk.Flow.from_inputs(workdir,
inputs=multi.split_datasets(),
task_class=flowtk.RelaxTask)
def build_ngkpt_flow(options):
"""
Crystalline silicon: computation of the total energy
Convergence with respect to the number of k points. Similar to tbase3_3.in
Args:
options: Command line options.
Return:
Abinit Flow object.
"""
# Definition of the different grids
ngkpt_list = [(2, 2, 2), (4, 4, 4), (6, 6, 6), (8, 8, 8)]
# These shifts will be the same for all grids
shiftk = [
float(s)
for s in "0.5 0.5 0.5 0.5 0.0 0.0 0.0 0.5 0.0 0.0 0.0 0.5".split()
]
# Build MultiDataset object (container of `ndtset` inputs).
# Structure is initialized from CIF file.
multi = abilab.MultiDataset(structure=abidata.cif_file("si.cif"),
pseudos=abidata.pseudos("14si.pspnc"),
ndtset=len(ngkpt_list))
# These variables are the same in each input.
multi.set_vars(ecut=8, toldfe=1e-6, diemac=12.0, iomode=3)
# Each input has its own value of `ngkpt`. shiftk is constant.
for i, ngkpt in enumerate(ngkpt_list):
multi[i].set_kmesh(ngkpt=ngkpt, shiftk=shiftk)
workdir = options.workdir if (options
and options.workdir) else "flow_base3_ngkpt"
# Split the inputs by calling multi.datasets() and pass the list of inputs to Flow.from_inputs.
return flowtk.Flow.from_inputs(workdir, inputs=multi.split_datasets())
def make_relax_flow(structure_file=None):
"""
Build and return a flow that perform a structural relaxation for different k-point samplings.
"""
ngkpt_list = [[3, 3, 2], [6, 6, 4], [8, 8, 6]]
if structure_file is None:
structure = abilab.Structure.from_file(abidata.cif_file("gan2.cif"))
else:
structure = abilab.Structure.from_file(structure_file)
multi = abilab.MultiDataset(structure=structure,
pseudos=get_pseudos(structure),
ndtset=len(ngkpt_list))
# Add mnemonics to input file.
multi.set_mnemonics(True)
# Global variables
multi.set_vars(
ecut=20,
tolrff=5.0e-2,
nstep=30,
optcell=2,
ionmov=3,
ntime=50,
dilatmx=1.05,
ecutsm=0.5,
tolmxf=5.0e-5,
)
for i, ngkpt in enumerate(ngkpt_list):
multi[i].set_kmesh(ngkpt=ngkpt, shiftk=[0, 0, 0])
return abilab.Flow.from_inputs("flow_gan_relax",
inputs=multi.split_datasets(),
task_class=abilab.RelaxTask)
def make_scf_nscf_inputs(structure, pseudos, paral_kgb=1):
"""return GS, NSCF (band structure), and DOSes input."""
multi = abilab.MultiDataset(structure, pseudos=pseudos, ndtset=5)
# Global variables
global_vars = dict(
ecut=10,
nband=11,
timopt=-1,
occopt=4, # Marzari smearing
tsmear=0.03,
paral_kgb=paral_kgb,
)
multi.set_vars(global_vars)
# Dataset 1 (GS run)
multi[0].set_kmesh(ngkpt=[8, 8, 8], shiftk=structure.calc_shiftk())
multi[0].set_vars(tolvrs=1e-6)
# Dataset 2 (NSCF Band Structure)
multi[1].set_kpath(ndivsm=6)
multi[1].set_vars(tolwfr=1e-12)
# Dos calculations with increasing k-point sampling.
for i, nksmall in enumerate([4, 8, 16]):
multi[i + 2].set_vars(
iscf=-3, # NSCF calculation
ngkpt=structure.calc_ngkpt(nksmall),
shiftk=[0.0, 0.0, 0.0],
tolwfr=1.0e-10,
)
# return GS, NSCF (band structure), DOSes input.
return multi.split_datasets()
def make_relax_flow():
# Structural relaxation for different k-point samplings.
ngkpt_list = [(2, 2, 2), (4, 4, 4)]
multi = abilab.MultiDataset(structure=abidata.cif_file("si.cif"),
pseudos=abidata.pseudos("14si.pspnc"),
ndtset=len(ngkpt_list))
# Global variables
multi.set_vars(
ecut=10,
tolvrs=1e-9,
optcell=1,
ionmov=3,
ntime=10,
dilatmx=1.05,
ecutsm=0.5,
)
for i, ngkpt in enumerate(ngkpt_list):
multi[i].set_kmesh(ngkpt=ngkpt, shiftk=[0, 0, 0])
return RelaxFlow.from_inputs("flow_base3_relax",
inputs=multi.split_datasets(),
task_class=flowtk.RelaxTask)
def make_inputs(options):
structure = abilab.Structure.from_abivars(unit_cell)
if options.paw:
raise RuntimeError("PAW is not implemented")
else:
pseudos = abidata.pseudos("26fe.pspnc", "83-Bi.GGA.fhi", '8o.pspnc')
#pseudos = ["fe.pot", "bi.pot", 'o.pot']
gs_inp = abilab.MultiDataset(structure, pseudos=pseudos, ndtset=2)
gs_inp.set_vars(global_vars)
gs_inp.set_vars(timopt=-1, kptopt=3, mem_test=0)
gs_inp[0].set_vars(tolvrs=1.0e-18)
gs_inp[1].set_vars(
iscf=-2,
tolwfr=1.0e-22,
nband=100,
nbdbuf=10,
)
return gs_inp.split_datasets()
def make_scf_nscf_inputs(structure, paral_kgb=1):
multi = abilab.MultiDataset(structure,
pseudos=data.pseudos("14si.pspnc"),
ndtset=2)
# Global variables
global_vars = dict(
ecut=6,
nband=8,
timopt=-1,
paral_kgb=0,
#nstep=4, # This is not enough to converge. Used to test the automatic restart.
nstep=10,
iomode=3,
)
multi.set_vars(global_vars)
# Dataset 1 (GS run)
multi[0].set_kmesh(ngkpt=[8, 8, 8], shiftk=[0, 0, 0])
multi[0].set_vars(tolvrs=1e-6)
# Dataset 2 (NSCF run)
kptbounds = [
[0.5, 0.0, 0.0], # L point
[0.0, 0.0, 0.0], # Gamma point
[0.0, 0.5, 0.5], # X point
]
multi[1].set_kpath(ndivsm=6, kptbounds=kptbounds)
multi[1].set_vars(tolwfr=1e-12)
# Generate two input files for the GS and the NSCF run
scf_input, nscf_input = multi.split_datasets()
return scf_input, nscf_input
def make_g0w0_inputs(ngkpt, tvars):
"""
Input files for the calculation of the GW corrections.
Returns: gs_input, nscf_input, scr_input, sigma_input
"""
multi = abilab.MultiDataset(structure=abidata.cif_file("si.cif"),
pseudos=abidata.pseudos("14si.pspnc"), ndtset=4)
# This grid is the most economical, but does not contain the Gamma point.
scf_kmesh = dict(
ngkpt=ngkpt,
shiftk=[0.5, 0.5, 0.5,
0.5, 0.0, 0.0,
0.0, 0.5, 0.0,
0.0, 0.0, 0.5]
)
# This grid contains the Gamma point, which is the point at which
# we will compute the (direct) band gap.
gw_kmesh = dict(
ngkpt=ngkpt,
shiftk=[0.0, 0.0, 0.0,
0.0, 0.5, 0.5,
0.5, 0.0, 0.5,
0.5, 0.5, 0.0]
)
# Global variables. gw_kmesh is used in all datasets except DATASET 1.
ecut = 4
multi.set_vars(
ecut=ecut,
pawecutdg=ecut*2 if multi.ispaw else None,
istwfk="*1",
paral_kgb=tvars.paral_kgb,
gwpara=2,
)
multi.set_kmesh(**gw_kmesh)
# Dataset 1 (GS run)
multi[0].set_kmesh(**scf_kmesh)
multi[0].set_vars(tolvrs=1e-6, nband=4)
# Dataset 2 (NSCF run)
multi[1].set_vars(iscf=-2,
tolwfr=1e-10,
nband=10,
nbdbuf=2)
# Dataset3: Calculation of the screening.
multi[2].set_vars(
optdriver=3,
nband=8,
ecutwfn=ecut,
symchi=1,
inclvkb=0,
ecuteps=2.0,
)
# Dataset4: Calculation of the Self-Energy matrix elements (GW corrections)
kptgw = [
-2.50000000E-01, -2.50000000E-01, 0.00000000E+00,
-2.50000000E-01, 2.50000000E-01, 0.00000000E+00,
5.00000000E-01, 5.00000000E-01, 0.00000000E+00,
-2.50000000E-01, 5.00000000E-01, 2.50000000E-01,
5.00000000E-01, 0.00000000E+00, 0.00000000E+00,
0.00000000E+00, 0.00000000E+00, 0.00000000E+00,
]
multi[3].set_vars(
optdriver=4,
nband=10,
ecutwfn=ecut,
ecuteps=2.0,
ecutsigx=2.0,
symsigma=1,
#gw_qprange=0,
)
bdgw = [4, 5]
multi[3].set_kptgw(kptgw, bdgw)
return multi.split_datasets()
def make_inputs(paw=False):
# Crystalline silicon
# Calculation of the GW correction to the direct band gap in Gamma
# Dataset 1: ground state calculation
# Dataset 2: NSCF calculation
# Dataset 3: calculation of the screening
# Dataset 4: calculation of the Self-Energy matrix elements (GW corrections)
structure = abilab.Structure.from_file(abidata.cif_file("si.cif"))
pseudos = abidata.pseudos("14si.pspnc") if not paw else abidata.pseudos("Si.GGA_PBE-JTH-paw.xml")
multi = abilab.MultiDataset(structure, pseudos=pseudos, ndtset=4)
ecut = 14
multi.set_vars(
ecut=ecut,
pawecutdg=ecut*4 if paw else None,
timopt=-1,
istwfk="*1",
paral_kgb=0,
)
multi.set_kmesh(
ngkpt=[6,6,6],
shiftk=[0.0, 0.0, 0.0],
)
gs, nscf, scr, sigma = multi.split_datasets()
gs.set_vars(tolvrs=1e-6,
nband=4,)
# Dataset 2 (NSCF run)
# Here we select the second dataset directly with the syntax inp[2]
nscf.set_vars(iscf=-2,
tolwfr=1e-8,
nband=300,
nbdbuf=50,
)
# Dataset3: Calculation of the screening.
scr.set_vars(
optdriver=3,
gwcalctyp=9,
gwpara=2,
nband=25,
ecutwfn=ecut,
symchi=1,
awtr=2,
inclvkb=0,
ecuteps=4.0,
spmeth=1, # Use Hilbert transform : Im chi0 --> chi0.
nomegasf=250, # Number of points for Imchi0
nfreqre=50,
nfreqim=10,
freqremax="25 eV",
freqremin="0 eV",
)
# Dataset4: Calculation of the Self-Energy matrix elements (GW corrections)
sigma.set_vars(
optdriver=4,
gwcalctyp=9,
gwpara=2,
nband=35,
ecutwfn=ecut,
ecuteps=4.0,
ecutsigx=ecut,
symsigma=1,
gw_qprange=1,
)
return gs, nscf, scr, sigma
def build_flow(options, paral_kgb=0):
# Working directory (default is the name of the script with '.py' removed and "run_" replaced by "flow_")
workdir = options.workdir
if not options.workdir:
workdir = os.path.basename(__file__).replace(".py", "").replace(
"run_", "flow_")
multi = abilab.MultiDataset(structure=data.structure_from_ucell("GaAs"),
pseudos=data.pseudos("31ga.pspnc",
"33as.pspnc"),
ndtset=5)
# Global variables
kmesh = dict(ngkpt=[4, 4, 4],
nshiftk=4,
shiftk=[[0.5, 0.5, 0.5], [0.5, 0.0, 0.0], [0.0, 0.5, 0.0],
[0.0, 0.0, 0.5]])
global_vars = dict(ecut=2, paral_kgb=paral_kgb)
global_vars.update(kmesh)
multi.set_vars(global_vars)
# Dataset 1 (GS run)
multi[0].set_vars(
tolvrs=1e-6,
nband=4,
)
# NSCF run with large number of bands, and points in the the full BZ
multi[1].set_vars(
iscf=-2,
nband=20,
nstep=25,
kptopt=1,
tolwfr=1.e-9,
#kptopt=3,
)
# Fourth dataset : ddk response function along axis 1
# Fifth dataset : ddk response function along axis 2
# Sixth dataset : ddk response function along axis 3
for dir in range(3):
rfdir = 3 * [0]
rfdir[dir] = 1
multi[2 + dir].set_vars(
iscf=-3,
nband=20,
nstep=1,
nline=0,
prtwf=3,
kptopt=3,
nqpt=1,
qpt=[0.0, 0.0, 0.0],
rfdir=rfdir,
rfelfd=2,
tolwfr=1.e-9,
)
scf_inp, nscf_inp, ddk1, ddk2, ddk3 = multi.split_datasets()
# Initialize the flow.
flow = abilab.Flow(workdir, manager=options.manager, remove=options.remove)
bands_work = abilab.BandStructureWork(scf_inp, nscf_inp)
flow.register_work(bands_work)
ddk_work = abilab.Work()
for inp in [ddk1, ddk2, ddk3]:
ddk_work.register_ddk_task(inp, deps={bands_work.nscf_task: "WFK"})
flow.register_work(ddk_work)
# Optic does not support MPI with ncpus > 1.
optic_input = abilab.OpticInput(
broadening=0.002, # Value of the smearing factor, in Hartree
domega=0.0003, # Frequency mesh.
maxomega=0.3,
scissor=0.000, # Scissor shift if needed, in Hartree
tolerance=0.002, # Tolerance on closeness of singularities (in Hartree)
num_lin_comp=
1, # Number of components of linear optic tensor to be computed
lin_comp=11, # Linear coefficients to be computed (x=1, y=2, z=3)
num_nonlin_comp=
2, # Number of components of nonlinear optic tensor to be computed
nonlin_comp=(123, 222), # Non-linear coefficients to be computed
)
# TODO
# Check is the order of the 1WF files is relevant. Can we use DDK files ordered
# in an arbitrary way or do we have to pass (x,y,z)?
optic_task = abilab.OpticTask(optic_input,
nscf_node=bands_work.nscf_task,
ddk_nodes=ddk_work)
flow.register_task(optic_task)
return flow
