def create_kpoints_from_distance(structure, distance, force_parity):
"""Generate a uniformly spaced kpoint mesh for a given structure.
The spacing between kpoints in reciprocal space is guaranteed to be at least the defined distance.
:param structure: the StructureData to which the mesh should apply
:param distance: a Float with the desired distance between kpoints in reciprocal space
:param force_parity: a Bool to specify whether the generated mesh should maintain parity
:returns: a KpointsData with the generated mesh
"""
from numpy import linalg
from aiida.orm import KpointsData
epsilon = 1E-5
kpoints = KpointsData()
kpoints.set_cell_from_structure(structure)
kpoints.set_kpoints_mesh_from_density(distance.value, force_parity=force_parity.value)
lengths_vector = [linalg.norm(vector) for vector in structure.cell]
lengths_kpoint = kpoints.get_kpoints_mesh()[0]
is_symmetric_cell = all(abs(length - lengths_vector[0]) < epsilon for length in lengths_vector)
is_symmetric_mesh = all(length == lengths_kpoint[0] for length in lengths_kpoint)
# If the vectors of the cell all have the same length, the kpoint mesh should be isotropic as well
if is_symmetric_cell and not is_symmetric_mesh:
nkpoints = max(lengths_kpoint)
kpoints.set_kpoints_mesh([nkpoints, nkpoints, nkpoints])
return kpoints
def validate_kpoints_mesh(ctx, param, value):
"""
Command line option validator for a kpoints mesh tuple. The value should be a tuple
of three positive integers out of which a KpointsData object will be created with
a mesh equal to the tuple.
:param ctx: internal context of the click.command
:param param: the click Parameter, i.e. either the Option or Argument to which the validator is hooked up
:param value: a tuple of three positive integers
:returns: a KpointsData instance
"""
# pylint: disable=unused-argument
from aiida.orm import KpointsData
if not value:
return None
if any([not isinstance(integer, int)
for integer in value]) or any([int(i) <= 0 for i in value]):
raise click.BadParameter(
'all values of the tuple should be positive greater than zero integers'
)
try:
kpoints = KpointsData()
kpoints.set_kpoints_mesh(value)
except ValueError as exception:
raise click.BadParameter(
'failed to create a KpointsData mesh out of {}\n{}'.format(
value, exception))
return kpoints
def get_shear_relax_builder(self,
shear_strain_ratio: float,
additional_relax_pks: list = None):
"""
Get relax builder for shear introduced relax twinboundary structure.
Args:
shear_strain_ratio (float): shear strain ratio
"""
twinboundary_analyzer = self.get_twinboundary_analyzer(
additional_relax_pks=additional_relax_pks)
cell = twinboundary_analyzer.get_shear_cell(
shear_strain_ratio=shear_strain_ratio,
is_standardize=False) # in order to get rotation matrix
std = StandardizeCell(cell=cell)
std_cell = std.get_standardized_cell(to_primitive=True,
no_idealize=False,
no_sort=True)
if additional_relax_pks is None:
rlx_pk = self.get_pks()['relax_pk']
else:
rlx_pk = additional_relax_pks[-1]
rlx_node = load_node(rlx_pk)
builder = rlx_node.get_builder_restart()
# fix kpoints
mesh, offset = map(np.array, builder.kpoints.get_kpoints_mesh())
orig_mesh = np.abs(
np.dot(np.linalg.inv(self._standardize.transformation_matrix),
mesh).astype(int))
orig_offset = np.round(np.abs(
np.dot(np.linalg.inv(std.transformation_matrix), offset)),
decimals=2)
std_mesh = np.abs(
np.dot(std.transformation_matrix, orig_mesh).astype(int))
std_offset = np.round(np.abs(
np.dot(std.transformation_matrix, orig_offset)),
decimals=2)
kpt = KpointsData()
kpt.set_kpoints_mesh(std_mesh, offset=std_offset)
builder.kpoints = kpt
# fix structure
builder.structure = get_aiida_structure(cell=std_cell)
# fix relax conf
builder.relax.convergence_max_iterations = Int(100)
builder.relax.positions = Bool(True)
builder.relax.shape = Bool(False)
builder.relax.volume = Bool(False)
builder.relax.convergence_positions = Float(1e-4)
builder.relax.force_cutoff = \
Float(AiidaRelaxWorkChain(node=rlx_node).get_max_force())
builder.metadata.label = "tbr:{} rlx:{} shr:{} std:{}".format(
self._pk, rlx_node.pk, shear_strain_ratio, True)
builder.metadata.description = \
"twinboundary_relax_pk:{} relax_pk:{} " \
"shear_strain_ratio:{} standardize:{}".format(
self._pk, rlx_node.pk, shear_strain_ratio, True)
return builder
def reuse_kpoints_grid(grid, lowest_pk=False):
"""
Retrieve previously stored kpoints mesh data node.
If there is no such ``KpointsData``, a new node will be created.
Will return the one with highest pk
:param grid: Grid to be retrieved
:param bool lowest_pk: If set to True will return the node with lowest pk
:returns: A KpointsData node representing the grid requested
"""
from aiida.orm import QueryBuilder
from aiida.orm import KpointsData
qbd = QueryBuilder()
qbd.append(KpointsData,
tag="kpoints",
filters={
"attributes.mesh.0": grid[0],
"attributes.mesh.1": grid[1],
"attributes.mesh.2": grid[2]
})
if lowest_pk:
order = "asc"
else:
order = "desc"
qbd.order_by({"kpoints": [{"id": {"order": order}}]})
if qbd.count() >= 1:
return qbd.first()[0]
kpoints = KpointsData()
kpoints.set_kpoints_mesh(grid)
return kpoints
def _generate_kpoints_mesh(npoints):
"""Return a `KpointsData` with a mesh of npoints in each direction."""
from aiida.orm import KpointsData
kpoints = KpointsData()
kpoints.set_kpoints_mesh([npoints] * 3)
return kpoints
def example_dft(code, pseudo_family):
"""Run simple silicon DFT calculation."""
print('Testing Abinit Total energy on Silicon using AbinitCalculation')
thisdir = os.path.dirname(os.path.realpath(__file__))
structure = StructureData(pymatgen=mg.core.Structure.from_file(
os.path.join(thisdir, 'files', 'Si.cif')))
pseudo_family = Group.objects.get(label=pseudo_family)
pseudos = pseudo_family.get_pseudos(structure=structure)
kpoints = KpointsData()
kpoints.set_cell_from_structure(structure)
kpoints.set_kpoints_mesh([2, 2, 2])
# kpoints.set_kpoints_mesh_from_density(2.0)
parameters_dict = {
'code':
code,
'structure':
structure,
'pseudos':
pseudos,
'kpoints':
kpoints,
'parameters':
Dict(
dict={
'ecut':
8.0, # Maximal kinetic energy cut-off, in Hartree
'nshiftk':
4, # of the reciprocal space (that form a BCC lattice !)
'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]],
'nstep':
20, # Maximal number of SCF cycles
'toldfe':
1.0e-6, # Will stop when, twice in a row, the difference
# between two consecutive evaluations of total energy
# differ by less than toldfe (in Hartree)
}),
'metadata': {
'options': {
'withmpi': True,
'max_wallclock_seconds': 2 * 60,
'resources': {
'num_machines': 1,
'num_mpiprocs_per_machine': 4,
}
}
}
}
print('Running calculation...')
run(AbinitCalculation, **parameters_dict)
def wf_getkpoints(aiida_structure, kptper_recipang):
from aiida.orm import KpointsData
def get_kmeshfrom_kptper_recipang(aiida_structure, kptper_recipang):
import numpy as np
kptper_recipang = kptper_recipang.value
ase_structure = aiida_structure.get_ase()
reci_cell = ase_structure.get_reciprocal_cell()
kmesh = [np.ceil(kptper_recipang * np.linalg.norm(reci_cell[i]))
for i in range(len(reci_cell))]
return kmesh
kpoints_mesh = get_kmeshfrom_kptper_recipang(aiida_structure, kptper_recipang)
kpoints = KpointsData()
kpoints.set_kpoints_mesh(kpoints_mesh)
return kpoints
def _get_kpoints(self, key, structure, previous_workchain):
from aiida.orm import KpointsData
if previous_workchain:
kpoints_mesh = KpointsData()
kpoints_mesh.set_cell_from_structure(structure)
previous_wc_kp = previous_workchain.inputs.kpoints
kpoints_mesh.set_kpoints_mesh(
previous_wc_kp.get_attribute('mesh'),
previous_wc_kp.get_attribute('offset'))
return kpoints_mesh
if 'kpoints' in self._protocols[key]:
kpoints_mesh = KpointsData()
kpoints_mesh.set_cell_from_structure(structure)
kp_dict = self._protocols[key]['kpoints']
if 'offset' in kp_dict:
kpoints_mesh.set_kpoints_mesh_from_density(
distance=kp_dict['distance'], offset=kp_dict['offset'])
else:
kpoints_mesh.set_kpoints_mesh_from_density(
distance=kp_dict['distance'])
return kpoints_mesh
return None
wannier_code = load_code(
"<CODE LABEL>") # Replace with the Wannier90 wannier.x code label
pw2wannier90_code = load_code(
"<CODE LABEL>") # Replace with the QE pw2wannier90.x code label
pseudo_family_name = "<UPF FAMILY NAME>" # Replace with the name of the pseudopotential family for SSSP efficiency
# GaAs structure
a = 5.68018817933178 # angstrom
structure = StructureData(
cell=[[-a / 2., 0, a / 2.], [0, a / 2., a / 2.], [-a / 2., a / 2., 0]])
structure.append_atom(symbols=['Ga'], position=(0., 0., 0.))
structure.append_atom(symbols=['As'], position=(-a / 4., a / 4., a / 4.))
# 4x4x4 k-points mesh for the SCF
kpoints_scf = KpointsData()
kpoints_scf.set_kpoints_mesh([4, 4, 4])
# 10x10x10 k-points mesh for the NSCF/Wannier90 calculations
kpoints_nscf = KpointsData()
kpoints_nscf.set_kpoints_mesh([10, 10, 10])
# k-points path for the band structure
kpoint_path = Dict(
dict={
'point_coords': {
'GAMMA': [0.0, 0.0, 0.0],
'K': [0.375, 0.375, 0.75],
'L': [0.5, 0.5, 0.5],
'U': [0.625, 0.25, 0.625],
'W': [0.5, 0.25, 0.75],
'X': [0.5, 0.0, 0.5]
def test_inp_gen_cell(gen_instance, sto_calc_inputs):
"""
Test generation of the inputs
"""
gen_instance.inputs = sto_calc_inputs
gen_instance.prepare_inputs()
assert 'symmetry_generate' in gen_instance.cell_file
assert "POSITIONS_ABS" in gen_instance.cell_file
assert "LATTICE_CART" in gen_instance.cell_file
assert isinstance(gen_instance.cell_file["cell_constraints"], list)
assert 'C9' in gen_instance.cell_file['SPECIES_POT'][0]
# Test extra-kpoints
from aiida.orm import KpointsData
kpn1 = KpointsData()
kpn1.set_kpoints_mesh((4, 4, 4))
gen_instance._include_extra_kpoints(kpn1, 'phonon', {
'task': ('phonon', ),
'need_weights': False
})
assert 'phonon_kpoint_mp_grid' in gen_instance.cell_file
kpn1.set_kpoints_mesh((
4,
4,
4,
), (0.25, 0.25, 0.25))
gen_instance._include_extra_kpoints(kpn1, 'phonon', {
'task': ('phonon', ),
'need_weights': False
})
assert 'phonon_kpoint_mp_offset' in gen_instance.cell_file
# Explicit kpoints, with/without the weights
kpn2 = KpointsData()
kpn_points = [[0, 0, 0], [0.5, 0.5, 0.5]]
kpn_weights = [0.3, 0.6]
kpn2.set_kpoints(kpn_points, weights=kpn_weights)
gen_instance._include_extra_kpoints(kpn2, 'bs', {
'task': ('bandstructure', ),
'need_weights': True
})
assert len(gen_instance.cell_file['BS_KPOINT_LIST'][0].split()) == 4
kpn2 = KpointsData()
kpn_points = [[0, 0, 0], [0.5, 0.5, 0.5]]
kpn2.set_kpoints(kpn_points)
gen_instance._include_extra_kpoints(kpn2, 'bs', {
'task': ('bandstructure', ),
'need_weights': True
})
assert len(gen_instance.cell_file['BS_KPOINT_LIST'][0].split()) == 4
assert float(gen_instance.cell_file['BS_KPOINT_LIST'][0].split()[3]) == 0.5
# No weights
gen_instance._include_extra_kpoints(kpn2, 'bs', {
'task': ('bandstructure', ),
'need_weights': False
})
assert len(gen_instance.cell_file['BS_KPOINT_LIST'][0].split()) == 3
assert 'BS_KPOINT_LIST' in gen_instance.cell_file
})
# Extra parameters that also go to the fdf file, but are related
# to the basis.
basis = Dict(
dict={
'pao-energy-shift': '300 meV',
'%block pao-basis-sizes': """
Si DZP
%endblock pao-basis-sizes""",
})
# Define the kpoints for the simulations. Note that this is not passed as
# a normal fdf parameter, it has "its own input"
kpoints = KpointsData()
kpoints.set_kpoints_mesh([14, 14, 14])
# Get the appropiate pseudos (in "real life", one could have a pseudos family defined
# in aiida database with `verdi data psf uploadfamily <path to folder> <family name>`)
# and then pass it as a simple string, Aiida will know which pseudos to use.
# See the pseudo_family in the aiida_siesta docs (link on top of the file)
pseudos_dict = {}
raw_pseudos = [("Si.psf", ['Si'])]
for fname, kinds in raw_pseudos:
absname = op.realpath(
op.join(op.dirname(__file__), "../fixtures/sample_psf", fname))
pseudo = PsfData.get_or_create(absname)
if not pseudo.is_stored:
print("\nCreated the pseudo for {}".format(kinds))
else:
print("\nUsing the pseudo for {} from DB: {}".format(kinds, pseudo.pk))
def phOriginalSubmit(uuid,
codename,
natlist,
qpoints=[[0.0, 0.0, 0.0]],
add_parameters={},
del_parameters={},
metadata={},
cluster_options={}):
"""
:code:`phOriginalSubmit` can submit a ph.x simulation to get the PDOS. It must follow a scf simulation.
:param uuid: (mandatory) The uuid of previous calculation. We will start our calculation from there. Because uuid
is the unique identification number for each CalcJobNode
:type uuid: python string object
:param codename: (mandatory) Represent the code for pw.x that you want to use. If you want to use the same as
previous calculation, then you need to use Str('')
:type codename: python string object
:param natlist: (mandatory) Assign the atoms which we want to do the vibrational frequency analysis.
:type natlist: python list object
:param qpoints: (optional, default = [[0.0, 0.0, 0.0]] It is like k-points, but useful when calculating
vibrational frequencies.
:type qpoints: python list object
:param add_parameters: (optional, default = {}) The desired parameters that you want to state, it can be incomplete,
because inside the function there is a default setting for parameters which can be used in
most cases, but if you have specific need, you can put that in parameters, the format is
similar as pw.x input file.
e.g. :code:`{'PROJWFC':{}}`
:type add_parameters: python dictionary object
:param del_parameters: (optional, default = {}) The tags that we would like to delete, for example if we do not
want to use spin-polarized simulation, then 'nspin' needs to be deleted. Same structure
as add_parameters.
e.g. :code:`{'PROJWFC': [key1, key2, key3]}`
:type del_parameters: python dictionary object
:param metadata: (optional, default = {}) The dictionary that contains information about metadata. For example:
label and description. label and description are mendatory.
:type metadata: python dictionary object
:param cluster_options: (optional, default = {}) The detailed option for the cluster. Different cluster may have
different settings. Only the following 3 keys can have effects: (1) resources (2)
account (3) queue_name
:type cluster_options: python dictionary object
:returns: uuid of the CalcJobNode object of the newest calculation.
"""
node = load_node(uuid=uuid)
# check whether it is nscf simulation
if node.inputs.parameters.get_dict()['CONTROL']['calculation'] != 'nscf':
return ValueError(
"You need to provide a nscf simulation with higher k-points.")
computer = codename.split('@')[1]
code = Code.get_from_string(codename)
ph_builder = code.get_builder()
# parameters
ph_parameter = Dict(dict=phParameter)
# add parameters in add_parameters
for key, value in add_parameters.items():
for key2, value2 in value.items():
ph_parameter[key][key2] = value2
# delete parameters in del_parameters
for key, value in del_parameters.items():
tmp = ph_parameter[key]
for key2 in value:
if key2 in tmp.keys():
tmp.pop(key2)
# set kpoints
qpts = KpointsData()
if len(qpoints) == 1:
qpts.set_kpoints_mesh(mesh=qpoints[0])
else:
qpts.set_kpoints_mesh(mesh=qpoints[0], offset=qpoints[1])
# set default options for slurm
# set first, then modify
ph_builder.metadata.options['resources'] = slurm_options[computer]['ph'][
'resources'] # in here machine =
# node
ph_builder.metadata.options['max_wallclock_seconds'] = slurm_options[
computer]['projwfc']['max_wallclock_seconds'] # in here machine = node
ph_builder.metadata.options['account'] = slurm_options[computer]['ph'][
'account'] # in here machine = node
ph_builder.metadata.options['scheduler_stderr'] = slurm_options[computer][
'ph']['scheduler_stderr']
ph_builder.metadata.options['scheduler_stderr'] = slurm_options[computer][
'ph']['scheduler_stderr']
ph_builder.metadata.options['queue_name'] = slurm_options[computer]['ph'][
'queue_name']
# reset cluster_options:
if len(cluster_options) > 0:
if 'resources' in cluster_options.keys():
ph_builder.metadata.options['resources'] = cluster_options[
'resources']
if 'account' in cluster_options.keys():
ph_builder.metadata.options['account'] = cluster_options['account']
if 'queue_name' in cluster_options.keys():
ph_builder.metadata.options['queue_name'] = cluster_options[
'queue_name']
ph_builder.parameters = Dict(dict=ph_parameter)
ph_builder.parent_folder = node.outputs.remote_folder
ph_builder.metadata.label = metadata['label']
ph_builder.metadata.description = metadata['description']
ph_builder.qpoints = qpts
calc = submit(ph_builder)
return calc.uuid
def qePwContinueSubmit(uuid,
pseudo_family,
pseudo_dict={},
codename='',
parent_folder=True,
add_parameters={},
del_parameters={},
kpoints=[],
cluster_options={},
metadata={},
settings_dict={}):
"""
`qePwContinueSubmit` will continue a simulation with similar or modified input parameters. All the parameters are
listed in the kwargs.
:param uuid: (mandatory) The uuid of previous calculation. We will start our calculation from there. Because uuid
is the unique identification number for each CalcJobNode
**Notice**: The uuid must be in the results dictionary, if not the program will shout KeyError.
And if you are testing, you could use assignValue to quickly create a dictionary that contains
the uuid that you want to continue.
:type uuid: python string object
:param pseudo_family: (mandatory) The pseudopotential family that you want to use. Make sure that you already have
that configured, otherwise an error will occur. This is mendatory.
:type pseudo_family: python string object
:param pseudo_dict: (optional, default = {}) Which contains the pseudopotential files that we want to use in the
simulation.
:type pseudo_dict: python dictionary object
:param codename: (optional, default = '') Represent the code for pw.x that you want to use. If you want to use the
same as previous calculation, then you need to use Str('')
:type codename: python string object
:param parent_folder: (optional, default = True) If parent_folder is True, then the calculation will start with the
output files from previous calculations.
:type parent_folder: python boolean object
:param add_parameters: (optional, default = {}) The desired parameters that you want to state, it can be incomplete,
because inside the function there is a default setting for parameters which can be used in
most cases, but if you have specific need, you can put that in parameters, the format is
similar as pw.x input file.
If you want to assign DFT+U and spin-polarization, you need to specify it on your own.
e.g. :code:`{'CONTROL':{}, 'SYSTEM':{}}`
**Notice**: more options in qePwOriginalSubmit function. In qePwContinueSubmit,
we assume that the user wants to restart from previous converged wave functions and
charge density, so we set ['CONTROL']['restart_mode']='restart', ['ELECTRON'][
'startingwfc']='file and ['ELECTRON']['startingpot']='file'.
:type add_parameters: python dictionary object
:param del_parameters: (optional, default = {})The tags that we would like to delete, for example if we do not
want to use spin-polarized simulation, then 'nspin' needs to be deleted. Same structure as
add_parameters.
e.g. :code:`{'CONTROL': [key1, key2, key3], 'SYSTEM': [key1, key2, key3]}`
:type del_parameters: python dictionary object
:param kpoints: (optional, default = []), if you want to keep the k-points for previous calculation, just use an
empty list :code:`[]`. The kpoints that you want to use, if the kpoints has only 1 list,
then it is the kpoint mesh, but if two lists are detected, then the first will be k-point mesh,
the second one will be the origin of k-point mesh.e.g. [[3, 3, 1]] or [[3, 3, 1],[0.5, 0.5, 0.5]]
:type kpoints: python list object
:param cluster_options: (optional, default = {}) The detailed option for the cluster. Different cluster may have
different settings. Only the following 3 keys can have effects: (1) resources (2)
account (3) queue_name. If value is :code:`{}`, then it means we will use previous settings
:type cluster_options: python dictionary object
:param metadata: (optional, default = {}) The dictionary that contains information about metadata. For example:
label and description.label and description are mendatory. If value is :code:`{}`,
then it means we will use previous settings.
:type metadata: python dictionary object
:param settings_dict: (optional, default = {}) which contains the additional information for the pw.x calculation.
e.g. Fixed atom, retrieving more files, parser options, etc. And the command-line options.
If value is :code:`{}`, then it means we will use previous settings.
:type settings_dict: python dictionary object
:returns: uuid of the CalcJobNode of the newest calculation.
"""
node = load_node(uuid=uuid)
if len(codename) == 0: # not going to change cluster
computer = node.computer.label
restart_builder = node.get_builder_restart() # get the restart_builder
else:
computer = codename.split('@')[1]
code = Code.get_from_string(codename)
restart_builder = code.get_builder()
parameters_tmp = deepcopy(node.inputs.parameters)
parameters_dict = parameters_tmp.get_dict()
calc_type = parameters_dict['CONTROL']['calculation']
# change the parameters (since this is the continuation of the previous calculation)
parameters_tmp['CONTROL']['restart_mode'] = 'restart'
parameters_tmp['ELECTRONS'][
'startingwfc'] = 'file' # from wave function in aiida.save
parameters_tmp['ELECTRONS'][
'startingpot'] = 'file' # from charge density in aiida.save
if calc_type == 'relax' or calc_type == 'vc-relax':
structure = node.outputs.output_structure
elif calc_type == 'scf' or calc_type == 'nscf':
structure = node.inputs.structure
# assign parameters in add_parameters
for key, value in add_parameters.items():
for key2, value2 in value.items():
parameters_tmp[key][key2] = value2
# delete parameters in del_parameters
for key, value in del_parameters.items():
tmp = parameters_tmp[key]
for key2 in value:
if key2 in tmp.keys():
tmp.pop(key2)
parameters_default = parameters_tmp
# reset the kpoints
if len(kpoints) > 0:
kpts = KpointsData()
if len(kpoints) == 1:
kpts.set_kpoints_mesh(mesh=kpoints[0])
else:
kpts.set_kpoints_mesh(mesh=kpoints[0], offset=kpoints[1])
else:
kpts = node.inputs.kpoints
# pseudopotential
# check whether pseudo_family and pseudo_dict are set at the same time, if true, then break
if len(pseudo_family) > 0 and len(pseudo_dict) > 0:
return ValueError(
"You cannot set pseudo_family and pseudo_dict at the same time")
if len(pseudo_family) == 0 and len(pseudo_dict) == 0:
return ValueError(
"You need to specify at least one in pseudo_family or pseudo_dict."
)
if len(pseudo_family) != 0:
restart_builder.pseudos = get_pseudos_from_structure(
structure, family_name=pseudo_family)
if len(pseudo_dict) != 0:
restart_builder.pseudos = pseudo_dict
# set default options for slurm
restart_builder.metadata.options['resources'] = slurm_options[computer][
'qe']['resources'] # in here machine = node
restart_builder.metadata.options['max_wallclock_seconds'] = slurm_options[
computer]['qe']['max_wallclock_seconds'] # in here machine = node
restart_builder.metadata.options['account'] = slurm_options[computer][
'qe']['account'] # in here machine = node
restart_builder.metadata.options['scheduler_stderr'] = slurm_options[
computer]['qe']['scheduler_stderr']
restart_builder.metadata.options['scheduler_stderr'] = slurm_options[
computer]['qe']['scheduler_stderr']
restart_builder.metadata.options['queue_name'] = slurm_options[computer][
'qe']['queue_name']
# reset cluster_options:
if len(cluster_options) > 0:
if 'resources' in cluster_options.keys():
restart_builder.metadata.options['resources'] = cluster_options[
'resources']
if 'account' in cluster_options.keys():
restart_builder.metadata.options['account'] = cluster_options[
'account']
if 'queue_name' in cluster_options.keys():
restart_builder.metadata.options['queue_name'] = cluster_options[
'queue_name']
# reset metadata
if len(metadata) > 0:
if 'label' in metadata.keys():
restart_builder.metadata.label = metadata['label']
else:
restart_builder.metadata.label = node.label
if 'description' in metadata.keys():
restart_builder.metadata.description = metadata['description']
else:
restart_builder.metadata.description = node.description
else:
restart_builder.metadata.label = node.label
restart_builder.metadata.description = node.description
# assign the parent_folder
if parent_folder:
restart_builder.parent_folder = node.outputs.remote_folder
# set settings_dict
if len(settings_dict) > 0:
pass
else:
settings_dict = node.inputs.settings.get_dict()
# submit the calculation
restart_builder.structure = structure
restart_builder.kpoints = kpts
restart_builder.parameters = parameters_default
restart_builder.settings = Dict(dict=settings_dict)
calc = submit(restart_builder)
return calc.uuid
def qePwOriginalSubmit(codename,
structure,
kpoints,
pseudo_family,
metadata,
pseudo_dict={},
add_parameters={},
del_parameters={},
cluster_options={},
settings_dict={}):
"""
:code:`qePwOriginalSubmit` will submit an original computational task to the desired computer by using certain code.
:param codename: (mandatory) A string represents the code for pw.x that you want to use.
:type codename: python string object
:param structure: (mandatory) The structure of your system.
:type structure: aiida.orm.StructureData object
:param add_parameters: (optional, default = {}) The desired parameters that you want to state, it can be
incomplete, because inside the function there is a default setting for parameters which can
be used in most cases, but if you have specific need, you can put that in parameters,
the format is similar as pw.x input file.
If you want to assign DFT+U and spin-polarization, you need to specify it on your own.
In Aiida, there is a very efficient way to specify the :code:`hubbard_u`,
:code:`starting_magnetization` and :code:`starting_ns_eigenvalue`. I give some examples
in below:
.. code-block:: python
# hubbard_u
'SYSTEM': {
'hubbard_u': {
'Fe': 5.0,
'Fe3': 5.0 # if you have different spins of same atom, then you should use
newStructure function to create the structure
},
'starting_magnetization': {
'Fe': 0.1,
'Fe3': 0.1,
},
'starting_ns_eigenvalue': [
[1, 1, 'Fe', 1.0] # represent: starting_ns_eigenvalue(1, 1, 1)=1.0
# others are the same, if you want to assign to Fe3, just replace Fe with Fe3.
]
}
:type add_parameters: python dictionary
:param del_parameters: (optional, default = {}) The tags that we would like to delete, for example if we do not
want to use spin-polarized simulation, then 'nspin' needs to be deleted. Same structure
as add_parameters.
e.g. :code:`{'CONTROL': [key1, key2, key3], 'SYSTEM': [key1, key2, key3]}`
:type del_parameters: python dictionary object
:param kpoints: (mandatory) The kpoints that you want to use, if the kpoints has only 1 list, then it is the kpoint
mesh, but if two lists are detected, then the first will be k-point mesh, the second one will be the
origin of k-point mesh.e.g. [[3, 3, 1]] or [[3, 3, 1],[0.5, 0.5, 0.5]]
:type kpoints: python list object
:param pseudo_family: (mandatory) The pseudopotential family that you want to use. Make sure that you already have
that configured, otherwise an error will occur.
:type pseudo_family: python string object.
:param pseudo_dict: (optional, default = {}) which contains the pseudopotential files that we want to use in the
simulation. In here it is very important to note that the path of the pseudopotential file
has to be in the absolute path.
e.g.
.. code-block:: python
pseudo_dict = {
'Fe': UpfData(absolute_path),
'Fe3': UpfData(absolute_path)
}
:type pseudo_dict: python dictionary object.
:param cluster_options: (optional, default = {}) The detailed option for the cluster. Different cluster may have
different settings. Only the following 3 keys can have effects: (1) resources (2) account
(3) queue_name
:type cluster_options: python dictionary object
:param metadata: (mandatory) The dictionary that contains information about metadata. For example: label and
description. label and description are mendatory.
e.g. :code:`{'label':{}, 'description':{}}`
:type metadata: python dictionary object
:param settings_dict: (optional, default = {}) which contains the additional information for the pw.x
calculation. e.g. Fixed atom, retrieving more files, parser options, etc. And the
command-line options.
:type settings_dict: python dictionary object
:returns: uuid of the new CalcJobNode
"""
code = Code.get_from_string(codename)
computer = codename.split('@')[1] # get the name of the cluster
pw_builder = code.get_builder()
# pseudopotential
# check whether pseudo_family and pseudo_dict are set at the same time, if true, then break
if len(pseudo_family) > 0 and len(pseudo_dict) > 0:
return ValueError(
"You cannot set pseudo_family and pseudo_dict at the same time")
if len(pseudo_family) == 0 and len(pseudo_dict) == 0:
return ValueError(
"You need to specify at least one in pseudo_family or pseudo_dict."
)
if len(pseudo_family) != 0:
pw_builder.pseudos = get_pseudos_from_structure(
structure, family_name=pseudo_family)
if len(pseudo_dict) != 0:
pw_builder.pseudos = pseudo_dict
# set kpoints
kpts = KpointsData()
if len(kpoints) == 1:
kpts.set_kpoints_mesh(mesh=kpoints[0])
else:
kpts.set_kpoints_mesh(mesh=kpoints[0], offset=kpoints[1])
# parameters
parameters_default = Dict(dict=pwParameter)
# add parameters in add_parameters
parameters_tmp = deepcopy(parameters_default)
for key, value in add_parameters.items():
for key2, value2 in value.items():
parameters_tmp[key][key2] = value2
# delete parameters in del_parameters
for key, value in del_parameters.items():
tmp = parameters_tmp[key]
for key2 in value:
if key2 in tmp.keys():
tmp.pop(key2)
else:
pass
parameters_default = parameters_tmp
# set labels and description
pw_builder.metadata.label = metadata['label']
pw_builder.metadata.description = metadata['description']
# set default options for slurm
pw_builder.metadata.options['resources'] = slurm_options[computer]['qe'][
'resources'] # in here machine = node
pw_builder.metadata.options['max_wallclock_seconds'] = slurm_options[
computer]['qe']['max_wallclock_seconds'] #in here machine = node
pw_builder.metadata.options['account'] = slurm_options[computer]['qe'][
'account'] # in here machine = node
pw_builder.metadata.options['scheduler_stderr'] = slurm_options[computer][
'qe']['scheduler_stderr']
pw_builder.metadata.options['scheduler_stderr'] = slurm_options[computer][
'qe']['scheduler_stderr']
pw_builder.metadata.options['queue_name'] = slurm_options[computer]['qe'][
'queue_name']
# revised by cluster_options
if len(cluster_options) > 0:
if 'resources' in cluster_options.keys():
pw_builder.metadata.options['resources'] = cluster_options[
'resources']
if 'account' in cluster_options.keys():
pw_builder.metadata.options['account'] = cluster_options['account']
if 'queue_name' in cluster_options.keys():
pw_builder.metadata.options['queue_name'] = cluster_options[
'queue_name']
# initialize the settings_dict
if len(settings_dict) == 0:
settings_dict['cmdline'] = ['-nk', '4']
else:
pass # do nothing
# get atomic occupations
if 'lda_plus_u' in parameters_default['SYSTEM']:
if parameters_default['SYSTEM']['lda_plus_u']:
settings_dict['parser_options'] = {
'parse_atomic_occupations': True
}
# launch the simulation
pw_builder.structure = structure
pw_builder.kpoints = kpts
pw_builder.parameters = parameters_default
pw_builder.settings = Dict(dict=settings_dict)
calc = submit(pw_builder)
return calc.uuid
def get_kpoints_mesh(self, mesh):
"""Factory for kpoints with mesh"""
kpoints_data = KpointsData()
kpoints_data.set_kpoints_mesh(mesh)
return kpoints_data
'electronic-temperature': '25 meV',
'write-forces': True,
})
#The basis set
basis = Dict(
dict={
'pao-energy-shift': '300 meV',
'%block pao-basis-sizes': """
Si DZP
%endblock pao-basis-sizes""",
})
#The kpoints
kpoints = KpointsData()
kpoints.set_kpoints_mesh([4, 4, 4])
#The pseudopotentials
pseudos_dict = {}
raw_pseudos = [("Si.psf", ['Si'])]
for fname, kinds in raw_pseudos:
absname = op.realpath(op.join(op.dirname(__file__), "../fixtures/sample_psf", fname))
pseudo = PsfData.get_or_create(absname)
if not pseudo.is_stored:
print("\nCreated the pseudo for {}".format(kinds))
else:
print("\nUsing the pseudo for {} from DB: {}".format(kinds, pseudo.pk))
for j in kinds:
pseudos_dict[j]=pseudo
'electronic-temperature': '25 meV',
'write-forces': True,
'mesh-cutoff': "200 Ry"
})
#The basis
basis = Dict(dict={
'pao-energy-shift': '100 meV',
'%block pao-basis-sizes': """
Si DZP
%endblock pao-basis-sizes""",
})
#The kpoints mesh
kpoints = KpointsData()
kpoints.set_kpoints_mesh([11, 11, 11])
##-------------------K-points for bands --------------------
bandskpoints = KpointsData()
bandskpoints = result['explicit_kpoints']
#The pseudopotentials
pseudos_dict = get_pseudos_from_structure(structure, 'nc-sr-04_pbe_standard-psf')
#Resources
options = {
"max_wallclock_seconds": 600,
# "withmpi" : True,
"resources": {
"num_machines": 1,
"num_mpiprocs_per_machine": 1,
