Source code for aiida_kkr.workflows.kkr_imp

# -*- coding: utf-8 -*-
"""
In this module you find the total workflow for a kkr impurity calculation
and some helper methods to do so with AiiDA
"""
from __future__ import print_function
from __future__ import absolute_import
from aiida.orm import Code, load_node, RemoteData, StructureData, Dict, SinglefileData, FolderData
from aiida.orm import CalcJobNode
from aiida.engine import WorkChain, ToContext, if_
from aiida.engine import calcfunction
from aiida_kkr.calculations.voro import VoronoiCalculation
from masci_tools.io.kkr_params import kkrparams
from aiida_kkr.tools.common_workfunctions import test_and_get_codenode, neworder_potential_wf, update_params_wf
from aiida_kkr.workflows.gf_writeout import kkr_flex_wc
from aiida_kkr.workflows.voro_start import kkr_startpot_wc
from aiida_kkr.workflows.kkr_imp_sub import kkr_imp_sub_wc, clean_sfd
import numpy as np
from aiida_kkr.tools.save_output_nodes import create_out_dict_node

__copyright__ = (u"Copyright (c), 2017, Forschungszentrum Jülich GmbH, "
                 "IAS-1/PGI-1, Germany. All rights reserved.")
__license__ = "MIT license, see LICENSE.txt file"
__version__ = "0.8.1"
__contributors__ = (u"Fabian Bertoldo", u"Philipp Rüßmann")
#TODO: generalize workflow to multiple impurities
#TODO: add additional checks for the input
#TODO: maybe work on a clearer outputnode structure


[docs]class kkr_imp_wc(WorkChain): """ Workchain of a kkrimp calculation starting either from scratch (with a structure and impurity_info node), or with a converged host potential and impurity startpotentials, ... to calculate the converged host-impurity potential of the system. :param options: (Dict), Workchain specifications :param wf_parameters: (Dict), specifications for the kkr impurity workflow :param voro_aux_parameters: (Dict), specification for the auxiliary voronoi calculation for the impurity :param kkrimp: (Code), mandatory: KKRimp code converging the host-imp-potential :param kkr: (Code), mandatory: KKR code for calculation the host potential :param voronoi: (Code), mandatory: Voronoi code to generate the impurity startpot :param remote_data_gf: (RemoteData): remote folder of a previous kkrflex calculation containing the flexfiles ... :param remote_data_host: (RemoteData): remote folder of a converged KKR host calculation :return workflow_info: (Dict), Information of workflow results :return last_calc_output_parameters: (Dict), output parameters of the last called calculation :return last_calc_info: (Dict), information of the last called calculation """ _workflowversion = __version__ _wf_label = 'kkr_imp_wc' _wf_description = 'Workflow for a KKRimp calculation' _options_default = {'queue_name' : '', # Queue name to submit jobs too 'resources': {"num_machines": 1}, # resources to allowcate for the job 'max_wallclock_seconds' : 60*60, # walltime after which the job gets killed (gets parsed to KKR)} 'custom_scheduler_commands' : '', # some additional scheduler commands 'withmpi' : True} # execute KKR with mpi or without _wf_default = kkr_imp_sub_wc.get_wf_defaults(silent=True) # settings for sub workflow (impurity convergence) _wf_default['retrieve_kkrflex'] = True # add control to retrieve kkrflex files to repository or leave on remote computer only _voro_aux_default = kkr_startpot_wc.get_wf_defaults(silent=True) # settings for vorostart workflow, used to generate starting potential
[docs] @classmethod def get_wf_defaults(self, silent=False): """ Print and return _wf_defaults dictionary. Can be used to easily create set of wf_parameters. returns _wf_defaults """ if not silent: print('Version of workflow: {}'.format(self._workflowversion)) return self._options_default, self._wf_default, self._voro_aux_default
[docs] @classmethod def define(cls, spec): """ Defines the outline of the workflow """ super(kkr_imp_wc, cls).define(spec) # define the inputs of the workflow spec.input("kkr", valid_type=Code, required=True, help="KKRhost code used to run GF writeout step.") spec.input("voronoi", valid_type=Code, required=True, help="Voronoi code used to create the impurity starting potential.") spec.input("kkrimp", valid_type=Code, required=True, help="KKRimp code used to converge the impurity calculation") spec.input("impurity_info", valid_type=Dict, required=True, help="Information of the impurity like position in the unit cell, screening cluster, atom type.") spec.input("remote_data_host", valid_type=RemoteData, required=False, help="RemoteData node of the converged host calculation. Used to write out the host Green function.") spec.input("remote_data_gf", valid_type=RemoteData, required=False, help="RemoteData node of precomputed host Green function.") spec.input("remote_data_gf_Efshift", valid_type=RemoteData, required=False, help="RemoteData node of precomputed host Green function with Fermi level shift (overwrite kkrflex_green and tmat files from first remote_data_gf node.") spec.input("options", valid_type=Dict, required=False, help="Options for running the codes (walltime etc.).") spec.input("options_voronoi", valid_type=Dict, required=False, help="Options for running the Voronoi code (if differing from general `options` node)") spec.input("voro_aux_parameters", valid_type=Dict, required=False, help="Parameters for the auxiliary voronoi starting potential workflow.") spec.input("wf_parameters", valid_type=Dict, required=False, help="Parameters for the KKRimp selfconsistency workflow.") spec.input("voro_params_overwrite", valid_type=Dict, required=False, help="If given, overwrite the some parameters used as input for auxiliary voronoi calculation of starting potential.") spec.input("params_kkr_overwrite", valid_type=Dict, required=False, help="Set some input parameters of the KKR calculation for the GF writeout step.") spec.input("startpot", valid_type=SinglefileData, required=False, help="Set starting potential (e.g. from preconverged calculation") # structure of the workflow spec.outline( cls.start, # initialize workflow if_(cls.validate_input)( # validate the input (if true, run_gf_writeout, else skip) cls.run_gf_writeout), # write out the host GF if_(cls.has_starting_potential_input)( # check if strarting potential exists in input already (otherwise create it) cls.run_voroaux, # calculate the auxiliary impurity potentials cls.construct_startpot), # construct the host-impurity startpotential cls.run_kkrimp_scf, # run the kkrimp_sub workflow to converge the host-imp startpot cls.return_results) # check if the calculation was successful and return the result nodes # define the possible exit codes spec.exit_code(141, 'ERROR_INVALID_INPUT_CODE', message="ERROR: one or more of the codes you provided do not " "use the necessary plugins: kkr.voro, kkr.kkr, kkr.kkrimp") spec.exit_code(142, 'ERROR_MISSING_KKRCODE', message="ERROR: since GF writeout step has to be conducted, " "'kkrcode' is needed as an input") spec.exit_code(143, 'ERROR_MISSING_REMOTE', message="ERROR: neither converged host remote nor GF writeout " "remote is given as an input. One of them is needed to " "proceed with this workflow!") spec.exit_code(144, 'ERROR_KKRIMP_SUB_WORKFLOW_FAILURE', message="ERROR: sub-workflow for KKRimp convergence failed") spec.exit_code(145, 'ERROR_KKRSTARTPOT_WORKFLOW_FAILURE', message="ERROR: sub-workflow Kkr_startpot failed (look for failure of voronoi calculation).") # define the outputs of the workflow spec.output('workflow_info', valid_type=Dict) spec.output('last_calc_output_parameters', valid_type=Dict) spec.output('last_calc_info', valid_type=Dict) spec.output('converged_potential', valid_type=SinglefileData, required=False) spec.output('remote_data_gf', valid_type=RemoteData)
[docs] def start(self): """ Init context and some parameters """ self.report('INFO: started KKR impurity workflow version {}' ''.format(self._workflowversion)) # get input parameters if 'options' in self.inputs: options_dict = self.inputs.options.get_dict() else: options_dict = self._options_default self.report('INFO: using default options') if 'wf_parameters' in self.inputs: wf_dict = self.inputs.wf_parameters.get_dict() else: wf_dict = self._wf_default self.report('INFO: using default workflow parameters for KKRimp scf cycle') if 'voro_aux_parameters' in self.inputs: voro_aux_dict = self.inputs.voro_aux_parameters.get_dict() else: voro_aux_dict = self._voro_aux_default self.report('INFO: using default workflow parameters for auxiliary voronoi calculation') # get parameters that will be added/overwritten for voronoi run if 'voro_params_overwrite' in self.inputs: self.ctx.change_voro_params = self.inputs.voro_params_overwrite.get_dict() else: self.ctx.change_voro_params = {} # set option parameters from input, or defaults self.ctx.withmpi = options_dict.get('withmpi', self._options_default['withmpi']) self.ctx.resources = options_dict.get('resources', self._options_default['resources']) self.ctx.max_wallclock_seconds = options_dict.get('max_wallclock_seconds', self._options_default['max_wallclock_seconds']) self.ctx.queue = options_dict.get('queue_name', self._options_default['queue_name']) self.ctx.custom_scheduler_commands = options_dict.get('custom_scheduler_commands', self._options_default['custom_scheduler_commands']) self.ctx.options_params_dict = Dict(dict={'withmpi': self.ctx.withmpi, 'resources': self.ctx.resources, 'max_wallclock_seconds': self.ctx.max_wallclock_seconds, 'queue_name': self.ctx.queue, 'custom_scheduler_commands': self.ctx.custom_scheduler_commands}) if 'options_voronoi' in self.inputs: self.ctx.options_params_dict_voronoi = self.inputs.options_voronoi.get_dict() self.report("INFO: Use different options for voronoi code ({})".format(self.ctx.options_params_dict_voronoi)) else: self.ctx.options_params_dict_voronoi = self.ctx.options_params_dict.get_dict() # set label and description of the workflow self.ctx.description_wf = self.inputs.get('description', 'Workflow for a KKR impurity calculation starting from a host-impurity potential') self.ctx.label_wf = self.inputs.get('label', 'kkr_imp_sub_wc') # set parameters for the auxiliary voronoi calculation self.ctx.voro_dos_params = voro_aux_dict.get('dos_params', self._voro_aux_default['dos_params']) self.ctx.voro_num_rerun = voro_aux_dict.get('num_rerun', self._voro_aux_default['num_rerun']) self.ctx.voro_fac_cls_increase = voro_aux_dict.get('fac_cls_increase', self._voro_aux_default['fac_cls_increase']) self.ctx.voro_natom_in_cls_min = voro_aux_dict.get('natom_in_cls_min', self._voro_aux_default['natom_in_cls_min']) self.ctx.voro_delta_e_min = voro_aux_dict.get('delta_e_min', self._voro_aux_default['delta_e_min']) self.ctx.voro_threshold_dos_zero = voro_aux_dict.get('threshold_dos_zero', self._voro_aux_default['threshold_dos_zero']) self.ctx.voro_check_dos = voro_aux_dict.get('check_dos', self._voro_aux_default['check_dos']) self.ctx.voro_delta_e_min_core_states = voro_aux_dict.get('delta_e_min_core_states', self._voro_aux_default['delta_e_min_core_states']) # set up new parameter dict to pass to voronoi subworkflow later self.ctx.voro_params_dict = Dict(dict={'queue_name': self.ctx.queue, 'resources': self.ctx.resources, 'max_wallclock_seconds': self.ctx.max_wallclock_seconds, 'withmpi': self.ctx.withmpi, 'custom_scheduler_commands': self.ctx.custom_scheduler_commands, 'dos_params': self.ctx.voro_dos_params, 'num_rerun': self.ctx.voro_num_rerun, 'fac_cls_increase': self.ctx.voro_fac_cls_increase, 'natom_in_cls_min': self.ctx.voro_natom_in_cls_min, 'delta_e_min': self.ctx.voro_delta_e_min, 'threshold_dos_zero': self.ctx.voro_threshold_dos_zero, 'check_dos': self.ctx.voro_check_dos, 'delta_e_min_core_states': self.ctx.voro_delta_e_min_core_states}) # set workflow parameters for the KKR impurity calculation self.ctx.kkr_runmax = wf_dict.get('kkr_runmax', self._wf_default['kkr_runmax']) self.ctx.convergence_criterion = wf_dict.get('convergence_criterion', self._wf_default['convergence_criterion']) self.ctx.mixreduce = wf_dict.get('mixreduce', self._wf_default['mixreduce']) self.ctx.threshold_aggressive_mixing = wf_dict.get('threshold_aggressive_mixing', self._wf_default['threshold_aggressive_mixing']) self.ctx.strmix = wf_dict.get('strmix', self._wf_default['strmix']) self.ctx.nsteps = wf_dict.get('nsteps', self._wf_default['nsteps']) self.ctx.aggressive_mix = wf_dict.get('aggressive_mix', self._wf_default['aggressive_mix']) self.ctx.aggrmix = wf_dict.get('aggrmix', self._wf_default['aggrmix']) self.ctx.broyden_number = wf_dict.get('broyden-number', self._wf_default['broyden-number']) self.ctx.mag_init = wf_dict.get('mag_init', self._wf_default['mag_init']) self.ctx.hfield = wf_dict.get('hfield', self._wf_default['hfield']) self.ctx.init_pos = wf_dict.get('init_pos', self._wf_default['init_pos']) self.ctx.accuracy_params = wf_dict.get('accuracy_params', self._wf_default['accuracy_params']) # set up new parameter dict to pass to kkrimp subworkflow later self.ctx.kkrimp_params_dict = Dict(dict={'nsteps': self.ctx.nsteps, 'kkr_runmax': self.ctx.kkr_runmax, 'threshold_aggressive_mixing': self.ctx.threshold_aggressive_mixing, 'convergence_criterion': self.ctx.convergence_criterion, 'mixreduce': self.ctx.mixreduce, 'strmix': self.ctx.strmix, 'aggressive_mix': self.ctx.aggressive_mix, 'aggrmix': self.ctx.aggrmix, 'broyden-number': self.ctx.broyden_number, 'mag_init': self.ctx.mag_init, 'hfield': self.ctx.hfield, 'init_pos': self.ctx.init_pos, 'accuracy_params': self.ctx.accuracy_params}) # retrieve option for kkrlfex files self.ctx.retrieve_kkrflex = wf_dict.get('retrieve_kkrflex', self._wf_default['retrieve_kkrflex']) # list of things that are cleaned if everything ran through self.ctx.sfd_final_cleanup = [] # report the chosen parameters to the user self.report('INFO: use the following parameter:\n' '\nGeneral settings\n' 'use mpi: {}\n' 'resources: {}\n' 'walltime (s): {}\n' 'queue name: {}\n' 'scheduler command: {}\n' 'description: {}\n' 'label: {}\n' 'parameters for the voroaux calculation: {}\n' 'parameters for the kkrimp scf: {}\n' ''.format(self.ctx.withmpi, self.ctx.resources, self.ctx.max_wallclock_seconds, self.ctx.queue, self.ctx.custom_scheduler_commands, self.ctx.description_wf, self.ctx.label_wf, self.ctx.voro_params_dict.get_dict(), self.ctx.kkrimp_params_dict.get_dict()))
[docs] def validate_input(self): """ Validate the input and catch possible errors from the input """ inputs = self.inputs inputs_ok = True if 'kkrimp' and 'voronoi' in inputs: try: test_and_get_codenode(inputs.kkrimp, 'kkr.kkrimp', use_exceptions=True) test_and_get_codenode(inputs.voronoi, 'kkr.voro', use_exceptions=True) except ValueError: inputs_ok = False self.report(self.exit_codes.ERROR_INVALID_INPUT_CODE) return self.exit_codes.ERROR_INVALID_INPUT_CODE elif 'kkr' in inputs: try: test_and_get_codenode(inputs.kkr, 'kkr.kkr', use_exceptions=True) except ValueError: inputs_ok = False self.report(self.exit_codes.ERROR_INVALID_INPUT_CODE) return self.exit_codes.ERROR_INVALID_INPUT_CODE if 'impurity_info' in inputs: self.report('INFO: found the following impurity info node in input: {}'.format(inputs.impurity_info.get_dict())) if 'remote_data_gf' in inputs and 'remote_data_host' in inputs: self.report('INFO: both converged host remote (pid: {}) and GF writeout remote (pid: {}) found in input. ' 'Converged host remote will not be used. Skip GF writeout step and ' 'start workflow with auxiliary voronoi calculations.' .format(inputs.remote_data_host.pk, inputs.remote_data_gf.pk)) do_gf_calc = False elif 'remote_data_host' in inputs: self.report('INFO: found converged host remote (pid: {}) in input. ' 'Start workflow by calculating the host GF.'.format(inputs.remote_data_host.pk)) if 'kkr' in inputs: do_gf_calc = True else: inputs_ok = False self.report(self.exit_codes.ERROR_MISSING_KKRCODE) return self.exit_codes.ERROR_MISSING_KKRCODE elif 'remote_data_gf' in inputs: remote_data_gf_node = load_node(inputs.remote_data_gf.pk) pk_kkrflex_writeoutcalc = remote_data_gf_node.get_incoming(link_label_filter=u'remote_folder').first().node.pk self.report('INFO: found remote_data node (pid: {}) from previous KKRFLEX calculation (pid: {}) in input. ' 'Skip GF writeout step and start workflow by auxiliary voronoi calculations.' .format(inputs.remote_data_gf.pk, pk_kkrflex_writeoutcalc)) do_gf_calc = False # check if second remote_data_gf node is given (used to overwrite Fermi level) if 'remote_data_gf_Efshift' in inputs: self.report('INFO: found remote_data_gf_Efshift (pid: {}) used to overwrite Fermi level.' .format(inputs.remote_data_gf_Efshift.pk)) else: inputs_ok = False self.report(self.exit_codes.ERROR_MISSING_REMOTE) return self.exit_codes.ERROR_MISSING_REMOTE self.ctx.do_gf_calc = do_gf_calc self.report('INFO: validated input successfully: {}. Do GF writeout calc: {}.'.format(inputs_ok, self.ctx.do_gf_calc)) return do_gf_calc
[docs] def run_gf_writeout(self): """ Run the gf_writeout workflow to calculate the host Green's function and the KKR flexfiles using the converged host remote folder and the impurity info node """ # collect inputs kkrcode = self.inputs.kkr imp_info = self.inputs.impurity_info converged_host_remote = self.inputs.remote_data_host options = self.ctx.options_params_dict # set label and description of the calc sub_label = 'GF writeout (conv. host pid: {}, imp_info pid: {})'.format(converged_host_remote.pk, imp_info.pk) sub_description = 'GF writeout sub workflow for kkrimp_wc using converged host remote data (pid: {}) and impurity_info node (pid: {})'.format(converged_host_remote.pk, imp_info.pk) builder = kkr_flex_wc.get_builder() builder.metadata.label = sub_label builder.metadata.description = sub_description builder.kkr = kkrcode builder.options = options builder.remote_data = converged_host_remote builder.impurity_info = imp_info if "params_kkr_overwrite" in self.inputs: builder.params_kkr_overwrite = self.inputs.params_kkr_overwrite # maybe set kkrflex_retrieve wf_params_gf = {} if not self.ctx.retrieve_kkrflex: wf_params_gf['retrieve_kkrflex'] = self.ctx.retrieve_kkrflex wf_params_gf = Dict(dict=wf_params_gf) builder.wf_parameters = wf_params_gf future = self.submit(builder) self.report('INFO: running GF writeout (pk: {})'.format(future.pk)) return ToContext(gf_writeout=future, last_calc_gf=future)
[docs] def has_starting_potential_input(self): """ check whether or not a starting potential needs to be created """ # initialize self.ctx.create_startpot = True # check if startpot exists in input # TODO maybe implement some consistency checks if 'startpot' in self.inputs: self.ctx.startpot_kkrimp = self.inputs.startpot self.ctx.create_startpot = False return self.ctx.create_startpot
[docs] def run_voroaux(self): """ Perform a voronoi calculation for every impurity charge using the structure from the converged KKR host calculation """ # TODO: generalize to multiple impurities # collect inputs vorocode = self.inputs.voronoi kkrcode = self.inputs.kkr imp_info = self.inputs.impurity_info voro_params = self.ctx.voro_params_dict if self.ctx.do_gf_calc: self.report('INFO: get converged host remote from inputs to extract structure for Voronoi calculation') converged_host_remote = self.inputs.remote_data_host else: self.report('INFO: get converged host remote from GF_host_calc and graph to extract structure for Voronoi calculation') remote_data_gf_node = load_node(self.inputs.remote_data_gf.pk) GF_host_calc = remote_data_gf_node.get_incoming(link_label_filter=u'remote_folder').first().node converged_host_remote = GF_host_calc.inputs.parent_folder # get previous kkr parameters following remote_folder->calc->parameters links prev_kkrparams = converged_host_remote.get_incoming(link_label_filter='remote_folder').first().node.get_incoming(link_label_filter='parameters').first().node calc_params = prev_kkrparams # set Fermi level for auxiliary impurity potential correctly (extract from EF that is used in impurity calc) set_efermi = self.get_ef_from_parent() self.report('INFO: set Fermi level in jellium starting potential to {}'.format(set_efermi)) # change voronoi parameters updatenode = Dict(dict={'ef_set': set_efermi, 'add_direct': True}) updatenode.label = 'Added Fermi energy' voro_params = update_params_wf(voro_params, updatenode) # add or overwrite some parameters (e.g. things that are only used by voronoi) calc_params_dict = calc_params.get_dict() # add some voronoi specific parameters automatically if found (RMTREF should also set RMTCORE to the same value) if '<RMTREF>' in list(calc_params_dict.keys()): self.report('INFO: add rmtcore to voro params') self.ctx.change_voro_params['<RMTCORE>'] = calc_params_dict['<RMTREF>'] self.report(self.ctx.change_voro_params) changed_params = False for key, val in self.ctx.change_voro_params.items(): if key in ['RUNOPT', 'TESTOPT']: opt_old = calc_params_dict.get(key, []) if type(val)!=list: val = [val] val = opt_old + val calc_params_dict[key] = val changed_params = True if changed_params: updatenode = Dict(dict=calc_params_dict) updatenode.label = 'Changed params for voroaux: {}'.format(list(self.ctx.change_voro_params.keys())) updatenode.description = 'Overwritten voronoi input parameter from kkr_imp_wc input.' calc_params = update_params_wf(calc_params, updatenode) # find host structure structure_host, voro_calc = VoronoiCalculation.find_parent_structure(converged_host_remote) # for every impurity, generate a structure and launch the voronoi workflow # to get the auxiliary impurity startpotentials self.ctx.voro_calcs = {} inter_struc = change_struc_imp_aux_wf(structure_host, imp_info) sub_label = 'voroaux calc for Zimp: {} in host-struc'.format(imp_info.get_dict().get('Zimp')) sub_description = 'Auxiliary voronoi calculation for an impurity with charge ' sub_description += '{} in the host structure from pid: {}'.format(imp_info.get_dict().get('Zimp'), converged_host_remote.pk) builder = kkr_startpot_wc.get_builder() builder.metadata.label = sub_label builder.metadata.description = sub_description builder.structure = inter_struc builder.voronoi = vorocode builder.kkr = kkrcode builder.wf_parameters = voro_params builder.calc_parameters = calc_params builder.options = Dict(dict=self.ctx.options_params_dict_voronoi) future = self.submit(builder) tmp_calcname = 'voro_aux_{}'.format(1) self.ctx.voro_calcs[tmp_calcname] = future self.report('INFO: running voro aux (Zimp= {}, pid: {})'.format(imp_info.get_dict().get('Zimp'), future.pk)) return ToContext(last_voro_calc=future)
[docs] def get_ef_from_parent(self): """ Extract Fermi level in Ry to which starting potential is set """ # first choose calculation to start from (3 possibilities) if self.ctx.do_gf_calc: parent_remote = self.inputs.remote_data_host elif 'remote_data_gf_Efshift' in self.inputs: parent_remote = load_node(self.inputs.remote_data_gf_Efshift.pk) else: parent_remote = load_node(self.inputs.remote_data_gf.pk) # now extract output parameters parent_calc = parent_remote.get_incoming(link_label_filter='remote_folder').first().node output_params = parent_calc.outputs.output_parameters.get_dict() # get fermi energy in Ry from output of KkrCalculation and return result set_efermi = output_params.get('fermi_energy') return set_efermi
[docs] def construct_startpot(self): """ Take the output of GF writeout and the converged host potential as well as the auxiliary startpotentials for the impurity to construct the startpotential for the KKR impurity sub workflow """ if not self.ctx.last_voro_calc.is_finished_ok: self.report(self.exit_codes.ERROR_KKRSTARTPOT_WORKFLOW_FAILURE) return self.exit_codes.ERROR_KKRSTARTPOT_WORKFLOW_FAILURE # collect all nodes necessary to construct the startpotential if self.ctx.do_gf_calc: GF_host_calc_pk = self.ctx.gf_writeout.outputs.workflow_info.get_dict().get('pk_flexcalc') self.report('GF_host_calc_pk: {}'.format(GF_host_calc_pk)) GF_host_calc = load_node(GF_host_calc_pk) converged_host_remote = self.inputs.remote_data_host else: remote_data_gf_node = load_node(self.inputs.remote_data_gf.pk) GF_host_calc = remote_data_gf_node.get_incoming(link_label_filter=u'remote_folder').first().node self.report('GF_host_calc_pk: {}'.format(GF_host_calc.pk)) # follow parent_folder link up to get remote folder converged_host_remote = GF_host_calc.get_incoming(link_label_filter='parent_folder').first().node # get remote folder of last voronoi calculation (i.e. the one from where we take the starting potential) print(self.ctx.last_voro_calc) all_nodes = self.ctx.last_voro_calc.get_outgoing(node_class=CalcJobNode).all() print(all_nodes) pk_last_voronoi = max([i.node.pk for i in all_nodes]) print(pk_last_voronoi) voro_calc_remote = load_node(pk_last_voronoi).outputs.remote_folder print(voro_calc_remote) print(load_node(pk_last_voronoi).outputs.retrieved.list_object_names()) print(GF_host_calc) print(GF_host_calc.outputs.retrieved.list_object_names()) # check wether or not calculation was taked from cached node caching_info = "INFO: cache_source of GF_host_calc node: {}".format(GF_host_calc.get_cache_source()) print(caching_info) self.report(caching_info) caching_info = "INFO: cache_source of voronoi node: {}".format(load_node(pk_last_voronoi).get_cache_source()) print(caching_info) self.report(caching_info) imp_info = self.inputs.impurity_info nspin = GF_host_calc.outputs.output_parameters.get_dict().get('nspin') ilayer_cent = imp_info.get_dict().get('ilayer_center', 0) # defaults to layer 0 # prepare settings dict potname_converged = 'out_potential' potname_impvorostart = 'output.pot' potname_imp = 'potential_imp' if nspin < 2: replacelist_pot2 = [[0,ilayer_cent]] else: replacelist_pot2 = [[0,2*ilayer_cent],[1,2*ilayer_cent+1]] try: neworder_pot1 = [int(i) for i in np.loadtxt(GF_host_calc.outputs.retrieved.open('scoef'), skiprows=1)[:,3]-1] except: neworder_pot1 = [int(np.loadtxt(GF_host_calc.outputs.retrieved.open('scoef'), skiprows=1)[3]-1)] settings_label = 'startpot_KKRimp for imp_info node {}'.format(imp_info.pk) settings_description = 'starting potential for impurity info: {}'.format(imp_info) settings = Dict(dict={'pot1': potname_converged, 'out_pot': potname_imp, 'neworder': neworder_pot1, 'pot2': potname_impvorostart, 'replace_newpos': replacelist_pot2, 'label': settings_label, 'description': settings_description}) print('startpot_kkrimp construction:', settings, converged_host_remote, voro_calc_remote) startpot_kkrimp = neworder_potential_wf(settings_node=settings, parent_calc_folder=converged_host_remote, parent_calc_folder2=voro_calc_remote) # add starting potential for kkrimp calculation to context self.ctx.startpot_kkrimp = startpot_kkrimp # add to list for final cleanup self.ctx.sfd_final_cleanup.append(startpot_kkrimp) self.report('INFO: created startpotential (pid: {}) for the impurity calculation ' 'by using information of the GF host calculation (pid: {}), the potential of the ' 'converged host system (remote pid: {}) and the potential of the auxiliary voronoi ' 'calculation (remote pid: {})'.format(startpot_kkrimp.pk, GF_host_calc.pk, converged_host_remote.pk, self.ctx.last_voro_calc.pk))
[docs] def run_kkrimp_scf(self): """ Uses both the previously generated host-impurity startpotential and the output from the GF writeout workflow as inputs to run the kkrimp_sub workflow in order to converge the host-impurity potential """ # collect all necessary input nodes kkrimpcode = self.inputs.kkrimp startpot = self.ctx.startpot_kkrimp kkrimp_params = self.ctx.kkrimp_params_dict options = self.ctx.options_params_dict imp_info = self.inputs.impurity_info if self.ctx.do_gf_calc: self.report('INFO: get GF remote from gf_writeout sub wf (pid: {})'.format(self.ctx.gf_writeout.pk)) gf_remote = self.ctx.gf_writeout.outputs.GF_host_remote else: self.report('INFO: get GF remote from input node (pid: {})'.format(self.inputs.remote_data_gf.pk)) gf_remote = self.inputs.remote_data_gf # save in context to return as output node self.ctx.gf_remote = gf_remote # set label and description sub_label = 'kkrimp_sub scf wf (GF host remote: {}, imp_info: {})'.format(gf_remote.pk, self.inputs.impurity_info.pk) sub_description = 'convergence of the host-impurity potential (pk: {}) using GF remote (pk: {})'.format(startpot.pk, gf_remote.pk) builder = kkr_imp_sub_wc.get_builder() builder.metadata.label=sub_label builder.metadata.description=sub_description builder.kkrimp=kkrimpcode builder.options=options builder.impurity_info=imp_info builder.host_imp_startpot=startpot builder.remote_data=gf_remote if 'remote_data_gf_Efshift' in self.inputs: builder.remote_data_Efshift = self.inputs.remote_data_gf_Efshift builder.wf_parameters=kkrimp_params future = self.submit(builder) self.report('INFO: running kkrimp_sub_wf (startpot: {}, GF_remote: {}, wf pid: {})'.format(startpot.pk, gf_remote.pk, future.pk)) return ToContext(kkrimp_scf_sub=future)
[docs] def return_results(self): """ Return the results and create all of the output nodes """ self.report('INFO: creating output nodes for the KKR impurity workflow ...') if self.ctx.kkrimp_scf_sub.is_finished_ok: link_nodes = {'results_scf_workflow': self.ctx.kkrimp_scf_sub.outputs.workflow_info} last_calc_pk = self.ctx.kkrimp_scf_sub.outputs.workflow_info.get_dict().get('last_calc_nodeinfo')['pk'] last_calc_output_params = load_node(last_calc_pk).outputs.output_parameters last_calc_info = self.ctx.kkrimp_scf_sub.outputs.workflow_info outputnode_dict = {} outputnode_dict['workflow_name'] = self.__class__.__name__ outputnode_dict['workflow_version'] = self._workflowversion if self.ctx.do_gf_calc: outputnode_dict['used_subworkflows'] = {'gf_writeout': self.ctx.gf_writeout.pk, 'kkr_imp_sub': self.ctx.kkrimp_scf_sub.pk} outputnode_dict['gf_wc_success'] = self.ctx.gf_writeout.outputs.workflow_info.get_dict().get('successful') link_nodes['gf_writeout'] = self.ctx.gf_writeout.outputs.workflow_info else: outputnode_dict['used_subworkflows'] = {'kkr_imp_sub': self.ctx.kkrimp_scf_sub.pk} if self.ctx.create_startpot: outputnode_dict['used_subworkflows']['auxiliary_voronoi'] = self.ctx.last_voro_calc.pk res_voro_info = self.ctx.last_voro_calc.outputs.results_vorostart_wc outputnode_dict['voro_wc_success'] = res_voro_info.get_dict().get('successful') link_nodes['results_startpot_workflow'] = self.ctx.last_voro_calc.outputs.results_vorostart_wc outputnode_dict['converged'] = last_calc_info.get_dict().get('convergence_reached') outputnode_dict['number_of_rms_steps'] = len(last_calc_info.get_dict().get('convergence_values_all_steps')) outputnode_dict['convergence_values_all_steps'] = last_calc_info.get_dict().get('convergence_values_all_steps') outputnode_dict['impurity_info'] = self.inputs.impurity_info.get_dict() outputnode_dict['kkrimp_wc_success'] = last_calc_info.get_dict().get('successful') outputnode_dict['last_calculation_uuid'] = load_node(last_calc_pk).uuid # create results node and link all sub-workflow output nodes outputnode_t = create_out_dict_node(Dict(dict=outputnode_dict), **link_nodes) outputnode_t.label = 'kkrimp_wc_inform' outputnode_t.description = 'Contains information for workflow' self.report('INFO: workflow_info node: {}'.format(outputnode_t.uuid)) self.out('workflow_info', outputnode_t) self.out('last_calc_output_parameters', last_calc_output_params) self.out('last_calc_info', last_calc_info) self.out('converged_potential', self.ctx.kkrimp_scf_sub.outputs.host_imp_pot) self.out('remote_data_gf', self.ctx.gf_remote) # cleanup things that are not needed anymore self.final_cleanup() # print final message before exiting self.report('INFO: created 3 output nodes for the KKR impurity workflow.') self.report('\n' '|------------------------------------------------------------------------------------------------------------------|\n' '|-------------------------------------| Done with the KKR impurity workflow! |-------------------------------------|\n' '|------------------------------------------------------------------------------------------------------------------|') else: self.report(self.exit_codes.ERROR_KKRIMP_SUB_WORKFLOW_FAILURE) return self.exit_codes.ERROR_KKRIMP_SUB_WORKFLOW_FAILURE
[docs] def final_cleanup(self): """ Remove unneeded files to save space """ for sfd in self.ctx.sfd_final_cleanup: clean_sfd(sfd) if self.ctx.create_startpot: kkr_startpot = self.ctx.last_voro_calc vorocalc = kkr_startpot.outputs.last_voronoi_remote.get_incoming(link_label_filter=u'remote_folder').first().node ret = vorocalc.outputs.retrieved for fname in ret.list_object_names(): if fname not in [VoronoiCalculation._OUTPUT_FILE_NAME, VoronoiCalculation._OUT_POTENTIAL_voronoi]: # delete all except vor default output file with ret.open(fname) as f: ret.delete_object(fname, force=True)
@calcfunction def change_struc_imp_aux_wf(struc, imp_info): # Note: works for single imp at center only! from aiida.common.constants import elements as PeriodicTableElements _atomic_numbers = {data['symbol']: num for num, data in PeriodicTableElements.items()} new_struc = StructureData(cell=struc.cell) new_struc.pbc = struc.pbc # take also pbc values from parent struc isite = 0 for site in struc.sites: sname = site.kind_name kind = struc.get_kind(sname) pos = site.position # intermediate fix to avoid crash for old structures with vacuum:'{H0.00X1.00}' # use atom kind='X' in the future for new structures if kind.get_symbols_string()=='{H0.00X1.00}': zatom = 0 else: zatom = _atomic_numbers[kind.get_symbols_string()] if isite == imp_info.get_dict().get('ilayer_center', 0): zatom = imp_info.get_dict().get('Zimp') if type(zatom)==list: zatom = zatom[0] # here this works for single impurity only! symbol = PeriodicTableElements.get(zatom).get('symbol') new_struc.append_atom(position=pos, symbols=symbol) isite += 1 return new_struc