Fleur Magnetic Anisotropy Energy Converge workchain

Import Example:

from aiida_fleur.workflows.mae_conv import FleurMaeConvWorkChain
#or
WorkflowFactory('fleur.mae_conv')

Description/Purpose

This workchain calculates Magnetic Anisotropy Energy over a given set of spin-quantization axes. The force-theorem is employed which means the workchain converges a reference charge density first then it submits a single FleurCalculation with a <forceTheorem> tag.

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

../../_images/mae_energies.png

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

../../_images/mae_conv.png

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

yes

wf_parameters

Dict

Settings of the workchain

no

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 -*-
'sqas': {'label': [0.0, 0.0]},    # sets theta, phi pairs to calculate
'soc_off': []                     # a list of atom labels to switch off SOC term

soc_off is a python list containing atoms labels. SOC is switched off for species, corresponding to the atom with a given label.

Note

It can be that the specie correspond to several atoms and soc_off switches off SOC 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.

An example of soc_off work:

'soc_off': ['458']

changes

<species name="Ir-2" element="Ir" atomicNumber="77" coreStates="17" magMom=".00000000" flipSpin="T">
  <mtSphere radius="2.52000000" gridPoints="747" logIncrement=".01800000"/>
  <atomicCutoffs lmax="8" lnonsphr="6"/>
  <energyParameters s="6" p="6" d="5" f="5"/>
  <prodBasis lcutm="4" lcutwf="8" select="4 0 4 2"/>
  <lo type="SCLO" l="1" n="5" eDeriv="0"/>
</species>
-----
<atomGroup species="Ir-2">
  <filmPos label="                 458">1.000/4.000 1.000/2.000 11.4074000502</filmPos>
  <force calculate="T" relaxXYZ="TTT"/>
  <nocoParams l_relax="F" alpha=".00000000" beta=".00000000" b_cons_x=".00000000" b_cons_y=".00000000"/>
</atomGroup>

to:

<species name="Ir-2" element="Ir" atomicNumber="77" coreStates="17" magMom=".00000000" flipSpin="T">
  <mtSphere radius="2.52000000" gridPoints="747" logIncrement=".01800000"/>
  <atomicCutoffs lmax="8" lnonsphr="6"/>
  <energyParameters s="6" p="6" d="5" f="5"/>
  <prodBasis lcutm="4" lcutwf="8" select="4 0 4 2"/>
  <special socscale="0.0"/>
  <lo type="SCLO" l="1" n="5" eDeriv="0"/>
</species>

As you can see, I was careful about “Ir-2” specie and it contained a single atom with a label 458. Please also refer to Setting up atom labels section to learn how to set labels up.

sqas is a python dictionary (key: value pairs). The key can be any string which sets a label of the SQA. value must be a list of 2 values: [theta, phi].

Output nodes

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

    {
    "errors": [],
    "failed_labels": [],
    "info": [],
    "mae": {
        "label": 0.001442720531486,
        "label2": 0.0
    },
    "mae_units": "eV",
    "sqa": {
        "label": [
            0.0,
            0.0
        ],
        "label2": [
            1.57079,
            1.57079
        ]
    },
    "warnings": [],
    "workflow_name": "FleurMaeConvWorkChain",
    "workflow_version": "0.1.0"
}

    Resulting MAE energies are listed according to given labels.

Layout

MAE converge always starts with a structure and a list of q-vectors to calculate. There is no way to continue from pre-converged charge density.

Error handling

A list of implemented exit codes:

Code

Meaning

230

Invalid workchain parameters

342

Convergence MAE calculation failed for all SQAs

343

Convergence MAE calculation failed for all SQAs

Example usage

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

from aiida_fleur.workflows.mae_conv import FleurMaeConvWorkChain

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

wf_para = Dict(dict={'sqas': {'label': [0.0, 0.0]},
                     'soc_off': []})


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,
          }


res = submit(FleurMaeConvWorkChain, **inputs)