def make_eos_flow(structure_file=None):
"""
Build and return a Flow to compute the equation of state
of an isotropic material for different k-point samplings.
"""
scale_volumes = np.arange(94, 108, 2) / 100.
if structure_file is None:
structure = abilab.Structure.from_file(abidata.cif_file("si.cif"))
else:
structure = abilab.Structure.from_file(structure_file)
multi = abilab.MultiDataset(structure=structure,
pseudos=get_pseudos(structure),
ndtset=len(scale_volumes))
# Global variables
multi.set_vars(ecut=16, tolvrs=1e-16)
multi.set_kmesh(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]])
for idt, scal_vol in enumerate(scale_volumes):
new_lattice = structure.lattice.scale(structure.volume * scal_vol)
new_structure = Structure(new_lattice, structure.species,
structure.frac_coords)
multi[idt].set_structure(new_structure)
eos_flow = abilab.Flow.from_inputs("flow_si_relax",
inputs=multi.split_datasets())
eos_flow.volumes = structure.volume * scale_volumes
return eos_flow
def make_eos_flow(structure_file=None):
"""
Build and return a Flow to compute the equation of state
of an isotropic material for different k-point samplings.
"""
scale_volumes = np.arange(94, 108, 2) / 100.
if structure_file is None:
structure = abilab.Structure.from_file(abidata.cif_file("si.cif"))
else:
structure = abilab.Structure.from_file(structure_file)
multi = abilab.MultiDataset(structure=structure, pseudos=get_pseudos(structure), ndtset=len(scale_volumes))
# Global variables
multi.set_vars(
ecut=16,
tolvrs=1e-16
)
multi.set_kmesh(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]])
for idt, scal_vol in enumerate(scale_volumes):
new_lattice = structure.lattice.scale(structure.volume*scal_vol)
new_structure = Structure(new_lattice, structure.species, structure.frac_coords)
multi[idt].set_structure(new_structure)
eos_flow = abilab.Flow.from_inputs("flow_si_relax", inputs=multi.split_datasets())
eos_flow.volumes = structure.volume * scale_volumes
return eos_flow
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_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_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 flowtk.Flow.from_inputs(workdir=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)
