Yambo Workflows Tutorial¶
The following shows how to use the workflows provided by the aiida yambo plugin.
YamboRestartWf¶
This is the basic workflow and will run a single yambo calculation, with a tolerance for failed calculations, and it will restart calculations that have failed due to
Time Exhaustion on the queue.
Memory errors.
After each calculation, this workflow will check the exit status(provided by the parser) and, if the calculation is failed, try to fix some parameters in order to resubmit the calculation and obtain results. As inputs, we have to provide the maximum number of attempted restarts.
Example usage:
# -*- coding: utf-8 -*-
from __future__ import absolute_import
from __future__ import print_function
import sys
import os
from aiida.plugins import DataFactory, CalculationFactory
from aiida.orm import List, Dict
from aiida.engine import submit
from aiida_yambo.workflows.yamborestart import YamboRestartWf
import argparse
def get_options():
parser = argparse.ArgumentParser(description='SCF calculation.')
parser.add_argument(
'--code',
type=int,
dest='code_pk',
required=True,
help='The yambo codename to use')
parser.add_argument(
'--precode',
type=int,
dest='precode_pk',
required=True,
help='The p2y codename to use')
parser.add_argument(
'--parent',
type=int,
dest='parent_pk',
required=True,
help='The parent to use')
parser.add_argument(
'--restarts',
type=int,
dest='max_restarts',
required=True,
help='maximum number of restarts')
parser.add_argument(
'--time',
type=int,
dest='max_wallclock_seconds',
required=False,
default=30*60,
help='max wallclock in seconds')
parser.add_argument(
'--nodes',
type=int,
dest='num_machines',
required=False,
default=1,
help='number of machines')
parser.add_argument(
'--mpi',
type=int,
dest='num_mpiprocs_per_machine',
required=False,
default=1,
help='number of mpi processes per machine')
parser.add_argument(
'--threads',
type=int,
dest='num_cores_per_mpiproc',
required=False,
default=1,
help='number of threads per mpi process')
parser.add_argument(
'--queue_name',
type=str,
dest='queue_name',
required=False,
default=None,
help='queue(PBS) or partition(SLURM) name')
parser.add_argument(
'--qos',
type=str,
dest='qos',
required=False,
default=None,
help='qos name')
parser.add_argument(
'--account',
type=str,
dest='account',
required=False,
default=None,
help='account name')
args = parser.parse_args()
###### setting the machine options ######
options = {
'code_pk': args.code_pk,
'precode_pk': args.precode_pk,
'parent_pk': args.parent_pk,
'max_restarts': args.max_restarts,
'max_wallclock_seconds': args.max_wallclock_seconds,
'resources': {
"num_machines": args.num_machines,
"num_mpiprocs_per_machine": args.num_mpiprocs_per_machine,
"num_cores_per_mpiproc": args.num_cores_per_mpiproc,
},
'custom_scheduler_commands': u"export OMP_NUM_THREADS="+str(args.num_cores_per_mpiproc),
}
if args.queue_name:
options['queue_name']=args.queue_name
if args.qos:
options['qos']=args.qos
if args.account:
options['account']=args.account
return options
def main(options):
###### setting the gw parameters ######
Dict = DataFactory('dict')
params_gw = {
'ppa': True,
'gw0': True,
'HF_and_locXC': True,
'em1d': True,
'Chimod': 'hartree',
#'EXXRLvcs': 40,
#'EXXRLvcs_units': 'Ry',
'BndsRnXp': [1, 10],
'NGsBlkXp': 2,
'NGsBlkXp_units': 'Ry',
'GbndRnge': [1, 10],
'DysSolver': "n",
'QPkrange': [[1, 1, 8, 9]],
'X_all_q_CPU': "1 1 1 1",
'X_all_q_ROLEs': "q k c v",
'SE_CPU': "1 1 1",
'SE_ROLEs': "q qp b",
}
params_gw = Dict(dict=params_gw)
###### creation of the YamboCalculation ######
builder = YamboCalculation.get_builder()
builder.gw.metadata.options.max_wallclock_seconds = \
options['max_wallclock_seconds']
builder.gw.metadata.options.resources = \
dict = options['resources']
if 'queue_name' in options:
builder.gw.metadata.options.queue_name = options['queue_name']
if 'qos' in options:
builder.gw.metadata.options.qos = options['qos']
if 'account' in options:
builder.metadata.options.account = options['account']
builder.gw.metadata.options.custom_scheduler_commands = options['custom_scheduler_commands']
builder.gw.parameters = params_gw
builder.gw.precode_parameters = Dict(dict={})
builder.gw.settings = Dict(dict={'INITIALISE': False, 'PARENT_DB': False})
builder.gw.code = load_node(options['code_pk'])
builder.gw.preprocessing_code = load_node(options['precode_pk'])
builder.parent_folder = load_node(options['parent_pk']).outputs.remote_folder
builder.max_restarts = Int(options['max_restarts'])
return builder
if __name__ == "__main__":
options = get_options()
builder = main(options)
running = submit(builder)
print("Submitted YamboCalculation; with pk=<{}>".format(running.pk))
YamboWorkflow¶
The YamboWorkflow provides the functionality to run GW calculation from the PW step, passing in all the required parameters for both the KS DFT step with PW and the subsequent GW step. It uses the PwBaseWorkchain from aiida-quantumespresso as a subworkflow to perform the first DFT part and the YamboRestartWf for the GW part.
Example usage.
#!/usr/bin/env runaiida
# -*- coding: utf-8 -*-
from __future__ import absolute_import
from __future__ import print_function
import sys
import os
from aiida.plugins import DataFactory, CalculationFactory
from aiida.orm import List, Dict
from aiida.engine import submit
from aiida_yambo.workflows.yambowf import YamboWorkflow
from aiida_quantumespresso.utils.pseudopotential import validate_and_prepare_pseudos_inputs
from ase import Atoms
import argparse
def get_options():
parser = argparse.ArgumentParser(description='YAMBO calculation.')
parser.add_argument(
'--yambocode',
type=int,
dest='yambocode_pk',
required=True,
help='The yambo(main code) codename to use')
parser.add_argument(
'--parent',
type=int,
dest='parent_pk',
required=True,
help='The parent to use')
parser.add_argument(
'--yamboprecode',
type=int,
dest='yamboprecode_pk',
required=False,
help='The precode to use')
parser.add_argument(
'--pwcode',
type=int,
dest='pwcode_pk',
required=False,
help='The pw to use')
parser.add_argument(
'--pseudo',
type=str,
dest='pseudo_family',
required=False,
help='The pseudo_family')
parser.add_argument(
'--time',
type=int,
dest='max_wallclock_seconds',
required=False,
default=30*60,
help='max wallclock in seconds')
parser.add_argument(
'--nodes',
type=int,
dest='num_machines',
required=False,
default=1,
help='number of machines')
parser.add_argument(
'--mpi',
type=int,
dest='num_mpiprocs_per_machine',
required=False,
default=1,
help='number of mpi processes per machine')
parser.add_argument(
'--threads',
type=int,
dest='num_cores_per_mpiproc',
required=False,
default=1,
help='number of threads per mpi process')
parser.add_argument(
'--queue_name',
type=str,
dest='queue_name',
required=False,
default=None,
help='queue(PBS) or partition(SLURM) name')
parser.add_argument(
'--qos',
type=str,
dest='qos',
required=False,
default=None,
help='qos name')
parser.add_argument(
'--account',
type=str,
dest='account',
required=False,
default=None,
help='account name')
args = parser.parse_args()
###### setting the machine options ######
options = {
'yambocode_pk': args.yambocode_pk,
'yamboprecode_pk': args.yamboprecode_pk,
'pwcode_pk': args.pwcode_pk,
'pseudo_family': args.pseudo_family,
'max_wallclock_seconds': args.max_wallclock_seconds,
'resources': {
"num_machines": args.num_machines,
"num_mpiprocs_per_machine": args.num_mpiprocs_per_machine,
"num_cores_per_mpiproc": args.num_cores_per_mpiproc,
},
'custom_scheduler_commands': u"export OMP_NUM_THREADS="+str(args.num_cores_per_mpiproc),
}
if args.parent_pk:
options['parent_pk']=args.parent_pk
if args.queue_name:
options['queue_name']=args.queue_name
if args.qos:
options['qos']=args.qos
if args.account:
options['account']=args.account
return options
def main(options):
###### setting the lattice structure ######
alat = 2.4955987320 # Angstrom
the_cell = [[1.000000*alat, 0.000000, 0.000000],
[-0.500000*alat, 0.866025*alat, 0.000000],
[0.000000, 0.000000, 6.4436359260]]
atoms = Atoms('BNNB', [(1.2477994910, 0.7204172280, 0.0000000000),
(-0.0000001250, 1.4408346720, 0.0000000000),
(1.2477994910, 0.7204172280, 3.2218179630),
(-0.0000001250,1.4408346720, 3.2218179630)],
cell = [1,1,1])
atoms.set_cell(the_cell, scale_atoms=False)
atoms.set_pbc([True,True,True])
StructureData = DataFactory('structure')
structure = StructureData(ase=atoms)
###### setting the kpoints mesh ######
KpointsData = DataFactory('array.kpoints')
kpoints = KpointsData()
kpoints.set_kpoints_mesh([6,6,2])
###### setting the scf parameters ######
Dict = DataFactory('dict')
params_scf = {
'CONTROL': {
'calculation': 'scf',
'verbosity': 'high',
'wf_collect': True
},
'SYSTEM': {
'ecutwfc': 130.,
'force_symmorphic': True,
'nbnd': 20
},
'ELECTRONS': {
'mixing_mode': 'plain',
'mixing_beta': 0.7,
'conv_thr': 1.e-8,
'diago_thr_init': 5.0e-6,
'diago_full_acc': True
},
}
parameter_scf = Dict(dict=params_scf)
params_nscf = {
'CONTROL': {
'calculation': 'nscf',
'verbosity': 'high',
'wf_collect': True
},
'SYSTEM': {
'ecutwfc': 130.,
'force_symmorphic': True,
'nbnd': 500
},
'ELECTRONS': {
'mixing_mode': 'plain',
'mixing_beta': 0.6,
'conv_thr': 1.e-8,
'diagonalization': 'david',
'diago_thr_init': 5.0e-6,
'diago_full_acc': True
},
}
parameter_nscf = Dict(dict=params_nscf)
KpointsData = DataFactory('array.kpoints')
kpoints = KpointsData()
kpoints.set_kpoints_mesh([6,6,2])
alat = 2.4955987320 # Angstrom
the_cell = [[1.000000*alat, 0.000000, 0.000000],
[-0.500000*alat, 0.866025*alat, 0.000000],
[0.000000, 0.000000, 6.4436359260]]
atoms = Atoms('BNNB', [(1.2477994910, 0.7204172280, 0.0000000000),
(-0.0000001250, 1.4408346720, 0.0000000000),
(1.2477994910, 0.7204172280, 3.2218179630),
(-0.0000001250,1.4408346720, 3.2218179630)],
cell = [1,1,1])
atoms.set_cell(the_cell, scale_atoms=False)
atoms.set_pbc([True,True,True])
StructureData = DataFactory('structure')
structure = StructureData(ase=atoms)
params_gw = {
'ppa': True,
'gw0': True,
'HF_and_locXC': True,
'em1d': True,
'Chimod': 'hartree',
#'EXXRLvcs': 40,
#'EXXRLvcs_units': 'Ry',
'BndsRnXp': [1, 60],
'NGsBlkXp': 2,
'NGsBlkXp_units': 'Ry',
'GbndRnge': [1, 60],
'DysSolver': "n",
'QPkrange': [[1, 1, 8, 9]],
'X_all_q_CPU': "1 1 1 1",
'X_all_q_ROLEs': "q k c v",
'SE_CPU': "1 1 1",
'SE_ROLEs': "q qp b",
}
params_gw = Dict(dict=params_gw)
builder = YamboWorkflow.get_builder()
##################scf+nscf part of the builder
builder.scf.pw.structure = structure
builder.scf.pw.parameters = parameter_scf
builder.scf.kpoints = kpoints
builder.scf.pw.metadata.options.max_wallclock_seconds = \
options['max_wallclock_seconds']
builder.scf.pw.metadata.options.resources = \
dict = options['resources']
if 'queue_name' in options:
builder.scf.pw.metadata.options.queue_name = options['queue_name']
if 'qos' in options:
builder.scf.pw.metadata.options.qos = options['qos']
if 'account' in options:
builder.scf.pw.metadata.options.account = options['account']
builder.scf.pw.metadata.options.custom_scheduler_commands = options['custom_scheduler_commands']
builder.nscf.pw.structure = builder.scf.pw.structure
builder.nscf.pw.parameters = parameter_nscf
builder.nscf.kpoints = builder.scf.kpoints
builder.nscf.pw.metadata = builder.scf.pw.metadata
builder.scf.pw.code = load_node(options['pwcode_pk'])
builder.nscf.pw.code = load_node(options['pwcode_pk'])
builder.scf.pw.pseudos = validate_and_prepare_pseudos_inputs(
builder.scf.pw.structure, pseudo_family = Str(options['pseudo_family'])
builder.nscf.pw.pseudos = builder.scf.pw.pseudos
##################yambo part of the builder
builder.yres.gw.metadata.options.max_wallclock_seconds = \
options['max_wallclock_seconds']
builder.yres.gw.metadata.options.resources = \
dict = options['resources']
if 'queue_name' in options:
builder.yres.gw.metadata.options.queue_name = options['queue_name']
if 'qos' in options:
builder.yres.gw.metadata.options.qos = options['qos']
if 'account' in options:
builder.yres.gw.metadata.options.account = options['account']
builder.yres.gw.parameters = params_gw
builder.yres.gw.precode_parameters = Dict(dict={})
builder.yres.gw.settings = Dict(dict={'INITIALISE': False, 'PARENT_DB': False})
builder.yres.max_restarts = Int(5)
builder.yres.gw.preprocessing_code = load_node(options['yamboprecode_pk'])
builder.yres.gw.code = load_node(options['yambocode_pk'])
builder.parent_folder = load_node(options['parent_pk']).outputs.remote_folder
if __name__ == "__main__":
options = get_options()
builder = main(options)
running = submit(builder)
print("Submitted YamboCalculation; with pk=<{}>".format(running.pk))
YamboConvergence¶
The YamboConvergence provides the functionality to run G0W0 calculations(using YamboWorkflow) over several parameters, and it can be used (for now) to perform multi-parameter investigation of the quasiparticle corrections. It is possible to accomplish automatic convergence by iteration over one or more parameter in a serial way, or to explore a provided 2-dimensional space of parameters, in order to perform a successive extrapolation of the results. Let’s see the case of automatic convergence over an arbitrary number of parameters (“type”: 1D_convergence):
Example usage:
#!/usr/bin/env runaiida
# -*- coding: utf-8 -*-
from __future__ import absolute_import
from __future__ import print_function
import sys
import os
from aiida.plugins import DataFactory, CalculationFactory
from aiida.orm import List, Dict
from aiida.engine import submit
from aiida_yambo.workflows.yambowf import YamboWorkflow
from aiida_quantumespresso.utils.pseudopotential import validate_and_prepare_pseudos_inputs
from ase import Atoms
import argparse
def get_options():
parser = argparse.ArgumentParser(description='YAMBO calculation.')
parser.add_argument(
'--yambocode',
type=int,
dest='yambocode_pk',
required=True,
help='The yambo(main code) codename to use')
parser.add_argument(
'--parent',
type=int,
dest='parent_pk',
required=True,
help='The parent to use')
parser.add_argument(
'--yamboprecode',
type=int,
dest='yamboprecode_pk',
required=False,
help='The precode to use')
parser.add_argument(
'--pwcode',
type=int,
dest='pwcode_pk',
required=False,
help='The pw to use')
parser.add_argument(
'--pseudo',
type=str,
dest='pseudo_family',
required=False,
help='The pseudo_family')
parser.add_argument(
'--time',
type=int,
dest='max_wallclock_seconds',
required=False,
default=30*60,
help='max wallclock in seconds')
parser.add_argument(
'--nodes',
type=int,
dest='num_machines',
required=False,
default=1,
help='number of machines')
parser.add_argument(
'--mpi',
type=int,
dest='num_mpiprocs_per_machine',
required=False,
default=1,
help='number of mpi processes per machine')
parser.add_argument(
'--threads',
type=int,
dest='num_cores_per_mpiproc',
required=False,
default=1,
help='number of threads per mpi process')
parser.add_argument(
'--queue_name',
type=str,
dest='queue_name',
required=False,
default=None,
help='queue(PBS) or partition(SLURM) name')
parser.add_argument(
'--qos',
type=str,
dest='qos',
required=False,
default=None,
help='qos name')
parser.add_argument(
'--account',
type=str,
dest='account',
required=False,
default=None,
help='account name')
args = parser.parse_args()
###### setting the machine options ######
options = {
'yambocode_pk': args.yambocode_pk,
'yamboprecode_pk': args.yamboprecode_pk,
'pwcode_pk': args.pwcode_pk,
'pseudo_family': args.pseudo_family,
'max_wallclock_seconds': args.max_wallclock_seconds,
'resources': {
"num_machines": args.num_machines,
"num_mpiprocs_per_machine": args.num_mpiprocs_per_machine,
"num_cores_per_mpiproc": args.num_cores_per_mpiproc,
},
'custom_scheduler_commands': u"export OMP_NUM_THREADS="+str(args.num_cores_per_mpiproc),
}
if args.parent_pk:
options['parent_pk']=args.parent_pk
if args.queue_name:
options['queue_name']=args.queue_name
if args.qos:
options['qos']=args.qos
if args.account:
options['account']=args.account
return options
def main(options):
###### setting the lattice structure ######
alat = 2.4955987320 # Angstrom
the_cell = [[1.000000*alat, 0.000000, 0.000000],
[-0.500000*alat, 0.866025*alat, 0.000000],
[0.000000, 0.000000, 6.4436359260]]
atoms = Atoms('BNNB', [(1.2477994910, 0.7204172280, 0.0000000000),
(-0.0000001250, 1.4408346720, 0.0000000000),
(1.2477994910, 0.7204172280, 3.2218179630),
(-0.0000001250,1.4408346720, 3.2218179630)],
cell = [1,1,1])
atoms.set_cell(the_cell, scale_atoms=False)
atoms.set_pbc([True,True,True])
StructureData = DataFactory('structure')
structure = StructureData(ase=atoms)
###### setting the kpoints mesh ######
KpointsData = DataFactory('array.kpoints')
kpoints = KpointsData()
kpoints.set_kpoints_mesh([6,6,2])
###### setting the scf parameters ######
Dict = DataFactory('dict')
params_scf = {
'CONTROL': {
'calculation': 'scf',
'verbosity': 'high',
'wf_collect': True
},
'SYSTEM': {
'ecutwfc': 130.,
'force_symmorphic': True,
'nbnd': 20
},
'ELECTRONS': {
'mixing_mode': 'plain',
'mixing_beta': 0.7,
'conv_thr': 1.e-8,
'diago_thr_init': 5.0e-6,
'diago_full_acc': True
},
}
parameter_scf = Dict(dict=params_scf)
params_nscf = {
'CONTROL': {
'calculation': 'nscf',
'verbosity': 'high',
'wf_collect': True
},
'SYSTEM': {
'ecutwfc': 130.,
'force_symmorphic': True,
'nbnd': 500
},
'ELECTRONS': {
'mixing_mode': 'plain',
'mixing_beta': 0.6,
'conv_thr': 1.e-8,
'diagonalization': 'david',
'diago_thr_init': 5.0e-6,
'diago_full_acc': True
},
}
parameter_nscf = Dict(dict=params_nscf)
KpointsData = DataFactory('array.kpoints')
kpoints = KpointsData()
kpoints.set_kpoints_mesh([6,6,2])
alat = 2.4955987320 # Angstrom
the_cell = [[1.000000*alat, 0.000000, 0.000000],
[-0.500000*alat, 0.866025*alat, 0.000000],
[0.000000, 0.000000, 6.4436359260]]
atoms = Atoms('BNNB', [(1.2477994910, 0.7204172280, 0.0000000000),
(-0.0000001250, 1.4408346720, 0.0000000000),
(1.2477994910, 0.7204172280, 3.2218179630),
(-0.0000001250,1.4408346720, 3.2218179630)],
cell = [1,1,1])
atoms.set_cell(the_cell, scale_atoms=False)
atoms.set_pbc([True,True,True])
StructureData = DataFactory('structure')
structure = StructureData(ase=atoms)
params_gw = {
'ppa': True,
'gw0': True,
'HF_and_locXC': True,
'em1d': True,
'Chimod': 'hartree',
#'EXXRLvcs': 40,
#'EXXRLvcs_units': 'Ry',
'BndsRnXp': [1, 60],
'NGsBlkXp': 2,
'NGsBlkXp_units': 'Ry',
'GbndRnge': [1, 60],
'DysSolver': "n",
'QPkrange': [[1, 1, 8, 9]],
'X_all_q_CPU': "1 1 1 1",
'X_all_q_ROLEs': "q k c v",
'SE_CPU': "1 1 1",
'SE_ROLEs': "q qp b",
}
params_gw = Dict(dict=params_gw)
builder = YamboWorkflow.get_builder()
##################scf+nscf part of the builder
builder.ywfl.scf.pw.structure = structure
builder.ywfl.scf.pw.parameters = parameter_scf
builder.kpoints = kpoints
builder.ywfl.scf.pw.metadata.options.max_wallclock_seconds = \
options['max_wallclock_seconds']
builder.ywfl.scf.pw.metadata.options.resources = \
dict = options['resources']
if 'queue_name' in options:
builder.ywfl.scf.pw.metadata.options.queue_name = options['queue_name']
if 'qos' in options:
builder.ywfl.scf.pw.metadata.options.qos = options['qos']
if 'account' in options:
builder.ywfl.scf.pw.metadata.options.account = options['account']
builder.ywfl.scf.pw.metadata.options.custom_scheduler_commands = options['custom_scheduler_commands']
builder.ywfl.nscf.pw.structure = builder.ywfl.scf.pw.structure
builder.ywfl.nscf.pw.parameters = parameter_nscf
builder.ywfl.nscf.pw.metadata = builder.ywfl.scf.pw.metadata
builder.ywfl.scf.pw.code = load_node(options['pwcode_pk'])
builder.ywfl.nscf.pw.code = load_node(options['pwcode_pk'])
builder.ywfl.scf.pw.pseudos = validate_and_prepare_pseudos_inputs(
builder.ywfl.scf.pw.structure, pseudo_family = Str(options['pseudo_family'])
builder.ywfl.nscf.pw.pseudos = builder.ywfl.scf.pw.pseudos
##################yambo part of the builder
builder.ywfl.yres.gw.metadata.options.max_wallclock_seconds = \
options['max_wallclock_seconds']
builder.ywfl.yres.gw.metadata.options.resources = \
dict = options['resources']
if 'queue_name' in options:
builder.ywfl.yres.gw.metadata.options.queue_name = options['queue_name']
if 'qos' in options:
builder.ywfl.yres.gw.metadata.options.qos = options['qos']
if 'account' in options:
builder.ywfl.yres.gw.metadata.options.account = options['account']
builder.ywfl.yres.gw.parameters = params_gw
builder.ywfl.yres.gw.precode_parameters = Dict(dict={})
builder.ywfl.yres.gw.settings = Dict(dict={'INITIALISE': False, 'PARENT_DB': False})
builder.ywfl.yres.max_restarts = Int(5)
builder.ywfl.yres.gw.preprocessing_code = load_node(options['yamboprecode_pk'])
builder.ywfl.yres.gw.code = load_node(options['yambocode_pk'])
builder.parent_folder = load_node(options['parent_pk']).outputs.remote_folder
builder.workflow_settings = Dict(dict={'type':'1D_convergence','what':'gap','where':[(1,8,1,9)],'where_in_words':['Gamma']})
#'what': 'single-levels','where':[(1,8),(1,9)]
var_to_conv = [{'var':['BndsRnXp','GbndRnge'],'delta': [[0,10],[0,10]], 'steps': 2, 'max_restarts': 3, \
'conv_thr': 0.2, 'conv_window': 2},
{'var':'NGsBlkXp','delta': 1, 'steps': 2, 'max_restarts': 3, \
'conv_thr': 0.2, 'conv_window': 2},
{'var':['BndsRnXp','GbndRnge'],'delta': [[0,10],[0,10]], 'steps': 2, 'max_restarts': 5, \
'conv_thr': 0.1, 'conv_window': 2},
{'var':'NGsBlkXp','delta': 1, 'steps': 2, 'max_restarts': 3, \
'conv_thr': 0.1, 'conv_window': 2},]
'''
{'var':'kpoints','delta': 1, 'steps': 2, 'max_restarts': 2, \
'conv_thr': 0.1, 'conv_window': 2, 'what':'gap','where':[(1,1)],}]
'''
for i in range(len(var_to_conv)):
print('{}-th variable will be {}'.format(i+1,var_to_conv[i]['var']))
builder.parameters_space = List(list = var_to_conv)
if __name__ == "__main__":
options = get_options()
builder = main(options)
running = submit(builder)
print("Submitted YamboCalculation; with pk=<{}>".format(running.pk))
As you can see, we have to provide workflow_settings, which encode some workflow logic:
{'type':'1D_convergence','what':'gap','where':[(k_v,vbM,k_c,cbm)],'where_in_words':['Gamma']})
The workflow submitted here looks for convergence on different parameters, searching each step a given parameter(1D). The quantity that tries to converge is the gap(‘what’) between given bands evaluated at fixed k-points. It is possible to choose also and indirect gap(notice that, changing the k-point mesh, the k-points will change index). The other functionality of the converge workflow is to converge single levels (‘gap’->’single-levels’, [(k_v,vbM,k_c,cbm)]->[(k,b)]), useful in the study of molecules. It is possible also to search convergence simultaneously for multiple gaps/levels, just adding tuples in the ‘where’ list. The workflow will take care of it and doesn’t stop until all the quantities are converged(or the maximum restarts are reached).
The complete workflow will return the results of the convergence iterations, as well as a final converged calculation, from which we can parse the converged parameters, and a complete story of all the calculations of the workflow with all the information provided.
The data can be plotted using a function in :
from aiida_yambo.utils.plot_utilities import plot_conv
plot_conv(<workflow_pk>,title='Gap at Gamma for bulk hBN')
Let’s see the case of 2-dimensional space exploration:
Example usage:
#!/usr/bin/env runaiida
# -*- coding: utf-8 -*-
from __future__ import absolute_import
from __future__ import print_function
import sys
import os
from aiida.plugins import DataFactory, CalculationFactory
from aiida.orm import List, Dict
from aiida.engine import submit
from aiida_yambo.workflows.yambowf import YamboWorkflow
from aiida_quantumespresso.utils.pseudopotential import validate_and_prepare_pseudos_inputs
from ase import Atoms
import argparse
def get_options():
parser = argparse.ArgumentParser(description='YAMBO calculation.')
parser.add_argument(
'--yambocode',
type=int,
dest='yambocode_pk',
required=True,
help='The yambo(main code) codename to use')
parser.add_argument(
'--parent',
type=int,
dest='parent_pk',
required=True,
help='The parent to use')
parser.add_argument(
'--yamboprecode',
type=int,
dest='yamboprecode_pk',
required=False,
help='The precode to use')
parser.add_argument(
'--pwcode',
type=int,
dest='pwcode_pk',
required=False,
help='The pw to use')
parser.add_argument(
'--pseudo',
type=str,
dest='pseudo_family',
required=False,
help='The pseudo_family')
parser.add_argument(
'--time',
type=int,
dest='max_wallclock_seconds',
required=False,
default=30*60,
help='max wallclock in seconds')
parser.add_argument(
'--nodes',
type=int,
dest='num_machines',
required=False,
default=1,
help='number of machines')
parser.add_argument(
'--mpi',
type=int,
dest='num_mpiprocs_per_machine',
required=False,
default=1,
help='number of mpi processes per machine')
parser.add_argument(
'--threads',
type=int,
dest='num_cores_per_mpiproc',
required=False,
default=1,
help='number of threads per mpi process')
parser.add_argument(
'--queue_name',
type=str,
dest='queue_name',
required=False,
default=None,
help='queue(PBS) or partition(SLURM) name')
parser.add_argument(
'--qos',
type=str,
dest='qos',
required=False,
default=None,
help='qos name')
parser.add_argument(
'--account',
type=str,
dest='account',
required=False,
default=None,
help='account name')
args = parser.parse_args()
###### setting the machine options ######
options = {
'yambocode_pk': args.yambocode_pk,
'yamboprecode_pk': args.yamboprecode_pk,
'pwcode_pk': args.pwcode_pk,
'pseudo_family': args.pseudo_family,
'max_wallclock_seconds': args.max_wallclock_seconds,
'resources': {
"num_machines": args.num_machines,
"num_mpiprocs_per_machine": args.num_mpiprocs_per_machine,
"num_cores_per_mpiproc": args.num_cores_per_mpiproc,
},
'custom_scheduler_commands': u"export OMP_NUM_THREADS="+str(args.num_cores_per_mpiproc),
}
if args.parent_pk:
options['parent_pk']=args.parent_pk
if args.queue_name:
options['queue_name']=args.queue_name
if args.qos:
options['qos']=args.qos
if args.account:
options['account']=args.account
return options
def main(options):
###### setting the lattice structure ######
alat = 2.4955987320 # Angstrom
the_cell = [[1.000000*alat, 0.000000, 0.000000],
[-0.500000*alat, 0.866025*alat, 0.000000],
[0.000000, 0.000000, 6.4436359260]]
atoms = Atoms('BNNB', [(1.2477994910, 0.7204172280, 0.0000000000),
(-0.0000001250, 1.4408346720, 0.0000000000),
(1.2477994910, 0.7204172280, 3.2218179630),
(-0.0000001250,1.4408346720, 3.2218179630)],
cell = [1,1,1])
atoms.set_cell(the_cell, scale_atoms=False)
atoms.set_pbc([True,True,True])
StructureData = DataFactory('structure')
structure = StructureData(ase=atoms)
###### setting the kpoints mesh ######
KpointsData = DataFactory('array.kpoints')
kpoints = KpointsData()
kpoints.set_kpoints_mesh([6,6,2])
###### setting the scf parameters ######
Dict = DataFactory('dict')
params_scf = {
'CONTROL': {
'calculation': 'scf',
'verbosity': 'high',
'wf_collect': True
},
'SYSTEM': {
'ecutwfc': 130.,
'force_symmorphic': True,
'nbnd': 20
},
'ELECTRONS': {
'mixing_mode': 'plain',
'mixing_beta': 0.7,
'conv_thr': 1.e-8,
'diago_thr_init': 5.0e-6,
'diago_full_acc': True
},
}
parameter_scf = Dict(dict=params_scf)
params_nscf = {
'CONTROL': {
'calculation': 'nscf',
'verbosity': 'high',
'wf_collect': True
},
'SYSTEM': {
'ecutwfc': 130.,
'force_symmorphic': True,
'nbnd': 500
},
'ELECTRONS': {
'mixing_mode': 'plain',
'mixing_beta': 0.6,
'conv_thr': 1.e-8,
'diagonalization': 'david',
'diago_thr_init': 5.0e-6,
'diago_full_acc': True
},
}
parameter_nscf = Dict(dict=params_nscf)
KpointsData = DataFactory('array.kpoints')
kpoints = KpointsData()
kpoints.set_kpoints_mesh([6,6,2])
alat = 2.4955987320 # Angstrom
the_cell = [[1.000000*alat, 0.000000, 0.000000],
[-0.500000*alat, 0.866025*alat, 0.000000],
[0.000000, 0.000000, 6.4436359260]]
atoms = Atoms('BNNB', [(1.2477994910, 0.7204172280, 0.0000000000),
(-0.0000001250, 1.4408346720, 0.0000000000),
(1.2477994910, 0.7204172280, 3.2218179630),
(-0.0000001250,1.4408346720, 3.2218179630)],
cell = [1,1,1])
atoms.set_cell(the_cell, scale_atoms=False)
atoms.set_pbc([True,True,True])
StructureData = DataFactory('structure')
structure = StructureData(ase=atoms)
params_gw = {
'ppa': True,
'gw0': True,
'HF_and_locXC': True,
'em1d': True,
'Chimod': 'hartree',
#'EXXRLvcs': 40,
#'EXXRLvcs_units': 'Ry',
'BndsRnXp': [1, 60],
'NGsBlkXp': 2,
'NGsBlkXp_units': 'Ry',
'GbndRnge': [1, 60],
'DysSolver': "n",
'QPkrange': [[1, 1, 8, 9]],
'X_all_q_CPU': "1 1 1 1",
'X_all_q_ROLEs': "q k c v",
'SE_CPU': "1 1 1",
'SE_ROLEs': "q qp b",
}
params_gw = Dict(dict=params_gw)
builder = YamboWorkflow.get_builder()
##################scf+nscf part of the builder
builder.ywfl.scf.pw.structure = structure
builder.ywfl.scf.pw.parameters = parameter_scf
builder.kpoints = kpoints
builder.ywfl.scf.pw.metadata.options.max_wallclock_seconds = \
options['max_wallclock_seconds']
builder.ywfl.scf.pw.metadata.options.resources = \
dict = options['resources']
if 'queue_name' in options:
builder.ywfl.scf.pw.metadata.options.queue_name = options['queue_name']
if 'qos' in options:
builder.ywfl.scf.pw.metadata.options.qos = options['qos']
if 'account' in options:
builder.ywfl.scf.pw.metadata.options.account = options['account']
builder.ywfl.scf.pw.metadata.options.custom_scheduler_commands = options['custom_scheduler_commands']
builder.ywfl.nscf.pw.structure = builder.ywfl.scf.pw.structure
builder.ywfl.nscf.pw.parameters = parameter_nscf
builder.ywfl.nscf.pw.metadata = builder.ywfl.scf.pw.metadata
builder.ywfl.scf.pw.code = load_node(options['pwcode_pk'])
builder.ywfl.nscf.pw.code = load_node(options['pwcode_pk'])
builder.ywfl.scf.pw.pseudos = validate_and_prepare_pseudos_inputs(
builder.ywfl.scf.pw.structure, pseudo_family = Str(options['pseudo_family'])
builder.ywfl.nscf.pw.pseudos = builder.ywfl.scf.pw.pseudos
##################yambo part of the builder
builder.ywfl.yres.gw.metadata.options.max_wallclock_seconds = \
options['max_wallclock_seconds']
builder.ywfl.yres.gw.metadata.options.resources = \
dict = options['resources']
if 'queue_name' in options:
builder.ywfl.yres.gw.metadata.options.queue_name = options['queue_name']
if 'qos' in options:
builder.ywfl.yres.gw.metadata.options.qos = options['qos']
if 'account' in options:
builder.ywfl.yres.gw.metadata.options.account = options['account']
builder.ywfl.yres.gw.parameters = params_gw
builder.ywfl.yres.gw.precode_parameters = Dict(dict={})
builder.ywfl.yres.gw.settings = Dict(dict={'INITIALISE': False, 'PARENT_DB': False})
builder.ywfl.yres.max_restarts = Int(5)
builder.ywfl.yres.gw.preprocessing_code = load_node(options['yamboprecode_pk'])
builder.ywfl.yres.gw.code = load_node(options['yambocode_pk'])
builder.parent_folder = load_node(options['parent_pk']).outputs.remote_folder
builder.workflow_settings = Dict(dict={'type':'2D_space','what':'gap','where':[(1,8,1,9)],'where_in_words':['Gamma']})
#'what': 'single-levels','where':[(1,8),(1,9)]
para_space = [{'var':['BndsRnXp','GbndRnge'],
'space': [[[1,10],[1,10]], \
[[1,50],[1,75]], \
[[1,75],[1,50]]], \
'max_restarts': 0,},
{'var':['BndsRnXp','GbndRnge'],
'space': [[[1,75],[1,75]]],
'max_restarts': 0,}]
for i in range(len(var_to_conv)):
print('{}-th variable will be {}'.format(i+1,var_to_conv[i]['var']))
builder.parameters_space = List(list = var_to_conv)
if __name__ == "__main__":
options = get_options()
builder = main(options)
running = submit(builder)
print("Submitted YamboCalculation; with pk=<{}>".format(running.pk))
It is possible to use some functions(that may be as a starting point for more complex parsing) to parse and plot the results of this type of workflow, in order to perform successive analysis.
