Fleur Spin-Spiral Dispersion workchain 

Current version: 0.2.0
Class: FleurSSDispWorkChain
String to pass to the WorkflowFactory(): fleur.ssdisp
Workflow type: Scientific workchain, force-theorem subgroup

Contents

Fleur Spin-Spiral Dispersion workchain

Import Example:

from aiida_fleur.workflows.ssdisp_conv import FleurSSDispWorkChain
#or
WorkflowFactory('fleur.ssdisp')

Description/Purpose 

This workchain calculates spin spiral energy dispersion over a given set of q-points. Resulting energies do not contain terms, corresponding to DMI energies. To take into account DMI, see the Fleur Dzyaloshinskii–Moriya Interaction energy workchain documentation.

In this workchain the force-theorem is employed which means the workchain converges a reference charge density first and then submits a single FleurCalculation with a <forceTheorem> tag. However, it is possible to specify inputs to use external pre-converged charge density to use it as a reference.

The task of the workchain us to calculate the energy difference between two or several structures having a different magnetisation profile:

To do this, the workchain employs the force theorem approach:

As it was mentioned, it is not always necessary to start with a structure. Setting up input parameters correctly (see Supported input configurations) one can start from a given FleuinpData, inp.xml or converged/not-fully-converged reference charge density.

Input nodes 

The FleurSSDispWorkChain employs exposed feature of the AiiDA, thus inputs for the nested SCF workchain should be passed in the namespace scf.

name	type	description	required
scf	namespace	inputs for nested SCF WorkChain	no
fleur	`Code`	Fleur code	yes
wf_parameters	`Dict`	Settings of the workchain	no
fleurinp	`FleurinpData`	FLEUR input	no
remote	`RemoteData`	Remote folder of another calculation	no
options	`Dict`	AiiDA options (computational resources)	no

Only fleur input is required. However, it does not mean that it is enough to specify fleur only. One must keep one of the supported input configurations described in the Supported input configurations section.

Workchain parameters and its defaults

`wf_parameters`

wf_parameters: Dict - Settings of the workflow behavior. All possible keys and their defaults are listed below:

# -*- coding: utf-8 -*-
'beta': {'all': 1.57079},            # see description below
'prop_dir': [1.0, 0.0, 0.0],         # sets a propagation direction of a q-vector
'q_vectors': [[0.0, 0.0, 0.0],       # set a set of q-vectors to calculate SSDispersion
              [0.125, 0.0, 0.0],
              [0.250, 0.0, 0.0],
              [0.375, 0.0, 0.0]],
'ref_qss': [0.0, 0.0, 0.0],          # sets a q-vector for the reference calculation
'inpxml_changes': []                 # additional changes before the FT step
'add_comp_para': {
    'only_even_MPI': False,          # True if suppress parallelisation having odd number of MPI
    'max_queue_nodes': 20,           # Max number of nodes allowed (used by automatic error fix)
    'max_queue_wallclock_sec': 86400 # Max number of walltime allowed (used by automatic error fix)
    },

beta is a python dictionary containing a key: value pairs. Each pair sets beta parameter in an inp.xml file. key specifies the atom label to change, key equal to ‘all’ sets all atoms groups. For example,

'beta' : {'222' : 1.57079}

changes

<atomGroup species="Fe-1">
  <filmPos label="                 222">.0000000000 .0000000000 -11.4075100502</filmPos>
  <force calculate="T" relaxXYZ="TTT"/>
  <nocoParams l_relax="F" alpha=".00000000" beta="0.00000" b_cons_x=".00000000" b_cons_y=".00000000"/>
</atomGroup>

to:

<atomGroup species="Fe-1">
  <filmPos label="                 222">.0000000000 .0000000000 -11.4075100502</filmPos>
  <force calculate="T" relaxXYZ="TTT"/>
  <nocoParams l_relax="F" alpha=".00000000" beta="1.57079" b_cons_x=".00000000" b_cons_y=".00000000"/>
</atomGroup>

Note

beta actually sets a beta parameter for a whole atomGroup. It can be that the atomGroup correspond to several atoms and beta switches sets beta for atoms that was not intended to change. You must be careful and make sure that several atoms do not correspond to a given specie.

prop_dir is used only to set up a spin spiral propagation direction to calc_parameters['qss'] which will be passed to SCF workchain and inpgen. It can be used to properly set up symmetry operations in the reference calculation.

`options`

options: Dict - AiiDA options (computational resources). Example:

'resources': {"num_machines": 1, "num_mpiprocs_per_machine": 1},
'max_wallclock_seconds': 6*60*60,
'queue_name': '',
'custom_scheduler_commands': '',
'import_sys_environment': False,
'environment_variables': {}

Output nodes 

out: Dict - Information of workflow results like success, last result node, list with convergence behavior

"energies": [
    0.0,
    0.00044082445345511,
],
"energy_units": "eV",
"errors": [],
"info": [],
"initial_structure": "a75459e5-f83e-4aff-a25d-595d938cb841",
"is_it_force_theorem": true,
"q_vectors": [
    [
        0.0,
        0.0,
        0.0
    ],
    [
        0.125,
        0.125,
        0.0
    ],
],
"warnings": [],
"workflow_name": "FleurSSDispWorkChain",
"workflow_version": "0.1.0"

Resulting Spin Spiral energies are sorted according to theirs q-vectors i.e. energies[N] corresponds to q_vectors[N].

Supported input configurations 

SSDisp workchain has several input combinations that implicitly define the workchain layout. Only scf, fleurinp and remote nodes control the behaviour, other input nodes are truly optional. Depending on the setup of the given inputs, one of three supported scenarios will happen:

scf:

SCF workchain will be submitted to converge the reference charge density which will be followed be the force theorem calculation. Depending on the inputs given in the SCF namespace, SCF will start from the structure or FleurinpData or will continue converging from the given remote_data (see details in SCF WorkChain).
remote:

Files which belong to the remote will be used for the direct submission of the force theorem calculation. inp.xml file will be converted to FleurinpData and charge density will be used as a reference charge density.
remote + fleurinp:

Charge density which belongs to remote will be used as a reference charge density, however inp.xml from the remote will be ignored. Instead, the given fleurinp will be used. The aforementioned input files will be used for direct submission of the force theorem calculation.

Other combinations of the input nodes scf, fleurinp and remote are forbidden.

Warning

One must follow one of the supported input configurations. To protect a user from the workchain misbehaviour, an error will be thrown if one specifies e.g. both scf and remote inputs because in this case the intention of the user is not clear either he/she wants to converge a new charge density or use the given one.

Error handling 

A list of implemented exit codes:

Code	Meaning
230	Invalid workchain parameters
231	Invalid input configuration
233	Input codes do not correspond to fleur or inpgen codes respectively.
235	Input file modification failed.
236	Input file was corrupted after modifications
334	Reference calculation failed.
335	Found no reference calculation remote repository.
336	Force theorem calculation failed.

Example usage 

# -*- coding: utf-8 -*-
from aiida.orm import load_node, Dict
from aiida.engine import submit

from aiida_fleur.workflows.ssdisp import FleurSSDispWorkChain

structure = load_node(STRUCTURE_PK)
fleur_code = load_node(FLEUR_PK)
inpgen_code = load_node(INPGEN_PK)

wf_para = Dict(
    dict={
        'beta': {
            'all': 1.57079
        },
        'prop_dir': [0.125, 0.125, 0.0],
        'q_vectors': [[0.0, 0.0, 0.0], [0.125, 0.125, 0.0], [0.250, 0.250, 0.0], [0.375, 0.375, 0.0],
                      [0.500, 0.500, 0.0]],
        'ref_qss': [0.0, 0.0, 0.0],
        'inpxml_changes': [],
        'add_comp_para': {
            'only_even_MPI': False,
            'max_queue_nodes': 20,
            'max_queue_wallclock_sec': 86400
        }
    })

options = Dict(
    dict={
        'resources': {
            'num_machines': 1,
            'num_mpiprocs_per_machine': 24
        },
        'queue_name': 'devel',
        'custom_scheduler_commands': '',
        'max_wallclock_seconds': 60 * 60
    })

parameters = Dict(
    dict={
        'atom': {
            'element': 'Pt',
            'lmax': 8
        },
        'atom2': {
            'element': 'Fe',
            'lmax': 8,
        },
        'comp': {
            'kmax': 3.8,
        },
        'kpt': {
            'div1': 20,
            'div2': 24,
            'div3': 1
        }
    })

wf_para_scf = {'fleur_runmax': 2, 'itmax_per_run': 120, 'density_converged': 0.2, 'mode': 'density'}

wf_para_scf = Dict(dict=wf_para_scf)

options_scf = Dict(
    dict={
        'resources': {
            'num_machines': 2,
            'num_mpiprocs_per_machine': 24
        },
        'queue_name': 'devel',
        'custom_scheduler_commands': '',
        'max_wallclock_seconds': 60 * 60
    })

inputs = {
    'scf': {
        'wf_parameters': wf_para_scf,
        'structure': structure,
        'calc_parameters': parameters,
        'options': options_scf,
        'inpgen': inpgen_code,
        'fleur': fleur_code
    },
    'wf_parameters': wf_para,
    'fleur': fleur_code,
    'options': options
}

res = submit(FleurSSDispWorkChain, **inputs)

Fleur Spin-Spiral Dispersion workchain

Description/Purpose

Input nodes

Workchain parameters and its defaults

wf_parameters

options

Output nodes

Supported input configurations

Error handling

Example usage