Fleur self-consistent Magnetic Anisotropy Energy workchain

  • Class: FleurMaeConvWorkChain
  • String to pass to the WorkflowFactory(): fleur.mae_conv
  • Workflow type: Scientific workchain, self-consistent subgroup
  • Aim: Calculate Magnetic Anisotropy Energies along given spin quantization axes
  • Computational demand: A Fleur SCF WorkChain for each SQA
  • Database footprint: Outputnode with information, full provenance, ~ 10+10*FLEUR Jobs nodes
  • File repository footprint: no addition to the JobCalculations run

Import Example:

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

Description/Purpose

This workchain calculates Magnetic Anisotropy Energy over a given set of spin-quantization axes. Charge density is converged for all SQAs which means a FleurScfWorkChain is submitted for each SQA. This requires more computational cost than FleurMaeWorkChain but gives more accurate results.

Input nodes

  • fleur: Code - Fleur code using the fleur.fleur plugin
  • inpgen, optional: Code - Inpgen code using the fleur.inpgen plugin
  • wf_parameters: Dict, optional - Settings of the workflow behavior
  • structure: StructureData, optional: Crystal structure data node.
  • calc_parameters: Dict, optional - FLAPW parameters, used by inpgen
  • options: Dict, optional - AiiDA options (queues, cpus)

Returns nodes

  • out (ParameterData): Information of workflow results like success, last result node, list with convergence behavior

Default inputs

Workflow parameters.

wf_parameters_dict = {
    'fleur_runmax': 10,
    'sqas': {'label' : [0.0, 0.0]},
    'alpha_mix': 0.05,
    'density_converged': 0.00005,
    'serial': False,
    'itmax_per_run': 30,
    'soc_off': [],
    'inpxml_changes': [],
}

Layout

Still has to be documented

Example usage

Still has to be documented

Output node example

Still has to be documented

Error handling

Still has to be documented