SIESTA STM workflow

Description

The SiestaSTMWorkchain workflow is functionally very similar to the SiestaBandsWorkchain workflow, but instead of a band structure, the analysis stage produces a file with the local density of states (LDOS) in an energy window. The LDOS can be seen as a “partial charge density” to which only those wavefunctions with eigenvalues in a given energy interval contribute. In the Tersoff-Hamann approximation, the LDOS can be used as a proxy for the simulation of STM experiments. The 3D LDOS file is then processed by a specialized program plstm to produce a plot of the LDOS in a 2D section at a given height in the unit cell (simulating the height of a STM tip), or a simulated topography map by recording the z coordinates with a given value of the LDOS.

The inputs to the STM workchain include a (possibly already relaxed) structure and the protocol specification. The energy window for the LDOS and the tip height or the LDOS iso-value can be in principle specified by the user if full control is desired (probably after evaluation of the results of the SiestaBandsWorkchain workflow), but for the purposes of a turn-key solution, a range of energies around the Fermi Level (or regions near to the HOMO and/or LUMO), and a range of heights should automatically be selected by the workflow and the results presented to the user for further consideration. The workflow executes the plstm program via an AiiDA plugin, which is also included in the aiida-siesta distribution. Its parser stage returns an AiiDA ArrayData object whose contents can be displayed by standard tools within AiiDA and the wider Python ecosystem.

Supported Siesta versions

At least 4.0.1 of the 4.0 series, and 4.1-b3 of the 4.1 series, which can be found in the development platform (https://gitlab.com/siesta-project/siesta/).

Inputs

• code, class Code

A database object representing a Siesta executable.

• stm_code, class Code

A code associated to the STM (plstm) plugin (siesta.stm)

• structure, class StructureData

A structure. See the plugin documentation for more details.

• height, class Float

The height of the plane at which the image is desired (in Ang).

• e1, class Float

The lower limit of the energy window for which the LDOS is to be computed (in eV).

• e2, class Float

The upper limit of the energy window for which the LDOS is to be computed (in eV).

• protocol, class Str

Either “standard” or “fast” at this point. Each has its own set of associated parameters.

• standard:

  {
     'kpoints_mesh_offset': [0., 0., 0.],
     'kpoints_mesh_density': 0.2,
     'dm_convergence_threshold': 1.0e-4,
     'forces_convergence_threshold': "0.02 eV/Ang",
     'min_meshcutoff': 100, # In Rydberg (!)
     'electronic_temperature': "25.0 meV",
     'md-type-of-run': "cg",
     'md-num-cg-steps': 10,
     'pseudo_familyname': 'lda-ag',
     'atomic_heuristics': {
         'H': { 'cutoff': 100 },
         'Si': { 'cutoff': 100 }
     },
     'basis': {
         'pao-energy-shift': '100 meV',
         'pao-basis-size': 'DZP'
     }
   }
  

• fast:

  {
     'kpoints_mesh_offset': [0., 0., 0.],
     'kpoints_mesh_density': 0.25,
     'dm_convergence_threshold': 1.0e-3,
     'forces_convergence_threshold': "0.2 eV/Ang",
     'min_meshcutoff': 80, # In Rydberg (!)
     'electronic_temperature': "25.0 meV",
     'md-type-of-run': "cg",
     'md-num-cg-steps': 8,
     'pseudo_familyname': 'lda-ag',
     'atomic_heuristics': {
         'H': { 'cutoff': 50 },
         'Si': { 'cutoff': 50 }
     },
     'basis': {
         'pao-energy-shift': '100 meV',
         'pao-basis-size': 'SZP'
     }
   }
  

The atomic_heuristics dictionary is intended to encode the peculiarities of particular elements. It is work in progress.

The basis section applies globally for now.

Outputs

• stm_array ArrayData

A collection of three 2D arrays (X, Y, Z) holding the section or topography information. They follow the meshgrid convention in Numpy. A contour plot can be generated with the get_stm_image.py script in the repository of examples.