Standard Siesta plugin¶

Description¶

A plugin for Siesta’s basic functionality.

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¶

Some examples are referenced in the following list. They are located in the folder aiida_siesta/examples/plugins/siesta.

code, class Code, Mandatory

A code object linked to a Siesta executable. If you setup the code Siesta4.0.1 on machine kelvin following the aiida guidelines, then the code is selected in this way:
```
codename = 'Siesta4.0.1@kelvin'
from aiida.orm import Code
code = Code.get_from_string(codename)
```

structure, class StructureData, Mandatory

A structure. Siesta employs “species labels” to implement special conditions (such as basis set characteristics) for specific atoms (e.g., surface atoms might have a richer basis set). This is implemented through the ‘name’ attribute of the Site objects. For example:

from aiida.orm import StructureData

alat = 15. # angstrom
cell = [[alat, 0., 0.,],
  [0., alat, 0.,],
  [0., 0., alat,],
 ]

 # Benzene molecule with a special carbon atom
 s = StructureData(cell=cell)
 s.append_atom(position=(0.000,0.000,0.468),symbols=['H'])
 s.append_atom(position=(0.000,0.000,1.620),symbols=['C'])
 s.append_atom(position=(0.000,-2.233,1.754),symbols=['H'])
 s.append_atom(position=(0.000,2.233,1.754),symbols=['H'])
 s.append_atom(position=(0.000,-1.225,2.327),symbols='C',name="Cred")
 s.append_atom(position=(0.000,1.225,2.327),symbols=['C'])
 s.append_atom(position=(0.000,-1.225,3.737),symbols=['C'])
 s.append_atom(position=(0.000,1.225,3.737),symbols=['C'])
 s.append_atom(position=(0.000,-2.233,4.311),symbols=['H'])
 s.append_atom(position=(0.000,2.233,4.311),symbols=['H'])
 s.append_atom(position=(0.000,0.000,4.442),symbols=['C'])
 s.append_atom(position=(0.000,0.000,5.604),symbols=['H'])

The class StructureData uses Angstrom as internal units, the cell and atom positions must be specified in Angstrom.

The StructureData can also import ase structures or pymatgen structures. These two tools can be used to load structure from files. See example example_cif_bands.py

parameters, class Dict, Mandatory

A dictionary with scalar fdf variables and blocks, which are the basic elements of any Siesta input file. A given Siesta fdf file can be cast almost directly into this dictionary form, except that some items (for structure data) are blocked. Any units are specified for now as part of the value string. Blocks are entered by using an appropriate key and Python’s multiline string constructor. For example:
```
from aiida.orm import Dict

parameters = Dict(dict={
  "mesh-cutoff": "200 Ry",
  "dm-tolerance": "0.0001",
      "%block example-block":
      """
      first line
      second line
      %endblock example-block""",
})
```
Note that Siesta fdf keywords allow ‘.’, ‘-‘, (or nothing) as internal separators. AiiDA does not allow the use of ‘.’ in nodes to be inserted in the database, so it should not be used in the input script (or removed before assigning the dictionary to the Dict instance). For legibility, a single dash (‘-‘) is suggested, as in the examples above.
pseudos, input namespace of class PsfData OR class PsmlData, Mandatory

The PsfData and PsmlData classes have been implemented along the lines of the Upf class for QE.

One pseudopotential file per atomic element. Several species (in the Siesta sense, which allows the same element to be treated differently according to its environment) can share the same pseudopotential. For the example above:
```
import os
from aiida_siesta.data.psf import PsfData

pseudo_file_to_species_map = [ ("C.psf", ['C', 'Cred']),("H.psf", ['H'])]
pseudos_dict = {}
for fname, kinds, in pseudo_file_to_species_map:
      absname = os.path.realpath(os.path.join("path/to/file",fname))
      pseudo, created = PsfData.get_or_create(absname, use_first=True)
      for j in kinds:
              pseudos_dict[j]=pseudo
```
Alternatively, a pseudo for every atomic species can be set with the use_pseudos_from_family method, if a family of pseudopotentials has been installed. For an example see example_psf_family.py

Note

The verdi command-line interface now supports entry points defined by external packages. We have implemented verdi data psf and verdi data psml suites of commands: uploadfamily, exportfamily, and listfamilies.

It can be argued that a single SiestaPseudo class, with psf and psml subclasses, might have been implemented. But the PsmlData class aims to transcend Siesta and to be used by other codes.
basis, class Dict, Optional

A dictionary specifically intended for basis set information. It follows the same structure as the parameters element, including the allowed use of fdf-block items. This raw interface allows a direct translation of the myriad basis-set options supported by the Siesta program. In future we might have a more structured input for basis-set information. An example:
```
from aiida.orm import Dict

basis_dict = {
'pao-basistype':'split',
'pao-splitnorm': 0.150,
'pao-energyshift': '0.020 Ry',
'%block pao-basis-sizes':
"""
C    SZP
Cred SZ
H    SZP
%endblock pao-basis-sizes""",
}

basis = Dict(dict=basis_dict)
```
In case no basis is set, the Siesta calculation will not include any basis specification and it will run with the default Basis: DZP plus (many) other defaults.
kpoints, class KpointsData, Optional

Reciprocal space points for the full sampling of the BZ during the self-consistent-field iteration. It must be given in mesh form. There is no support yet for Siesta’s kgrid-cutoff keyword:
```
from aiida.orm import KpointsData
kpoints=KpointsData()
kp_mesh = 5
mesh_displ = 0.5 #optional
kpoints.set_kpoints_mesh([kp_mesh,kp_mesh,kp_mesh],[mesh_displ,mesh_displ,mesh_displ])
```
If this node is not present, only the Gamma point is used for sampling.
bandskpoints, class KpointsData, Optional

Reciprocal space points for the calculation of bands. This keyword is meant to facilitate the management of kpoints exploiting the functionality of the class KpointsData. The full list of kpoints must be passed to the class and they must be in units of the reciprocal lattice vectors. Moreover the cell must be set in the KpointsData class.

This can be achieved manually listing a set of kpoints:
```
from aiida.orm import KpointsData
bandskpoints=KpointsData()
bandskpoints.set_cell(structure.cell, structure.pbc)
kpp = [(0.500,  0.250, 0.750), (0.500,  0.500, 0.500), (0., 0., 0.)]
bandskpoints.set_kpoints(kpp)
```
In this case the Siesta input will use the BandPoints block.

Alternatively (recommended) the high-symmetry path associated to the structure under investigation can be automatically generated through the aiida tool ‘get_explicit_kpoints_path’. Here how to use it:
```
from aiida.orm import KpointsData
bandskpoints=KpointsData()
from aiida.tools import get_explicit_kpoints_path
symmpath_parameters = Dict(dict={'reference_distance': 0.02})
kpresult = get_explicit_kpoints_path(s, **symmpath_parameters.get_dict())
bandskpoints = kpresult['explicit_kpoints']
```
Where ‘s’ in the input structure and ‘reference_distance’ is the distance between two subsequent kpoints. In this case the block BandLines is set in the Siesta calculation.

Note

‘get_explicit_kpoints_path’ make use of “SeeK-path”. Please cite the HPKOT paper if you use this tool. “SeeK-path” is a external utility, not a requirement for aiida-core, therefore it is not available by default. It can be easily installed using pip install seekpath. “SeeK-path” allows to determine canonical unit cells and k-point information in an easy way. For more general information, refer to the SeeK-path documentation.
Warning

as explained in the aiida documentation, “SeeK-path” might modify the structure to follow particular conventions and the generated kpoints might only apply on the internally generated ‘primitive_structure’ and not on the input structure that was provided. The correct way to use this tool is to use the generated ‘primitive_structure’ also for the Siesta calculation:
```
structure = kpresult['primitive_structure']
```
The final option (unrecommended) covers the situation when one really needs to maintain a specific convention for the structure or one needs to calculate the bands on a specific path that is not a high-symmetry direction, the following (very involved) option is available:
```
from aiida.orm import KpointsData
bandskpoints=KpointsData()
from aiida.tools.data.array.kpoints.legacy import get_explicit_kpoints_path as legacy_path
kpp = [('A',  (0.500,  0.250, 0.750), 'B', (0.500,  0.500, 0.500), 40),
('B', (0.500,  0.500, 0.500), 'C', (0., 0., 0.), 40)]
tmp=legacy_path(kpp)
bandskpoints.set_cell(structure.cell, structure.pbc)
bandskpoints.set_kpoints(tmp[3])
bandskpoints.labels=tmp[4]
```
The legacy “get_explicit_kpoints_path” shares only the name with the function in “aiida.tools”, but it is very different in scope.

The full list of cases can be explored looking at the example example_bands.py

Warning

The implementation relies on the correct description of the labels in the class KpointsData. Refrain from the use of ‘bandskpoints.labels’ in any other situation apart from the one described above. An incorrect use of the labels might result in an incorrect parsing of the bands.

If the keyword node bandskpoints is not present, no band structure is computed.
settings, class Dict , Optional

An optional dictionary that activates non-default operations. For a list of possible values to pass, see the section on advanced features.

Submitting the calculation¶

Once all the inputs above are set, the subsequent step consists in passing them to the calculation class and run/submit it.

First, the Siesta calculation class is loaded:

from aiida_siesta.calculations.siesta import SiestaCalculation
builder = SiestaCalculation.get_builder()

The inputs (defined as in the previous section) are passed to the builder:

builder.code = code
builder.structure = structure
builder.parameters = parameters
builder.pseudos = pseudos_dict
builder.basis = basis
builder.kpoints = kpoints
builder.bandskpoints = bandskpoints

Finally the resources for the calculation must be set, for instance:

builder.metadata.options.resources = {'num_machines': 1}
builder.metadata.options.max_wallclock_seconds = 1800

Optionally, label and description:

builder.metadata.label = 'My generic title'
builder.metadata.description 'My more detailed description'

To run the calculation in an interactive way:

from aiida.engine import run
results = run(builder)

Here the results variable will contain a dictionary containing all the nodes that were produced as output.

Another option is to submit it to the daemon:

from aiida.engine import submit
calc = submit(builder)

In this case, calc is the calculation node and not the results dictionary.

Note

In order to inspect the inputs created by AiiDA without actually running the calculation, we can perform a dry run of the submission process:

builder.metadata.dry_run = True
builder.metadata.store_provenance = False

This will create the input files, that are available for inspection.

Note

The use of the builder makes the process more intuitive, but it is not mandatory. The inputs can be provided as keywords argument when you launch the calculation, passing the calculation class as the first argument:

run(SiestaCalculation, structure=s, pseudos=pseudos, kpoints = kpoints, ...)

same syntax for the command submit.

A large set of examples covering some standard cases are in the folder aiida_siesta/examples/plugins/siesta. They can be run with:

runaiida example_name.py {--send, --dont-send} code@computer

The parameter –dont-send will activate the “dry run” option. In that case a test folder (submit_test) will be created, containing all the files that aiida generates automatically. The parameter –send will submit the example to the daemon. One of the two options needs to be present to run the script. The second argument contains the name of the code (code@computer) to use in the calculation. It must be a previously set up code, corresponding to a siesta executable.

Outputs¶

There are several output nodes that can be created by the plugin, according to the calculation details. All output nodes can be accessed with the calculation.outputs method.

output_parameters Dict

A dictionary with metadata, scalar result values, a warnings list, and possibly a timing section. Units are specified by means of an extra item with ‘_units’ appended to the key:
```
{
  "siesta:Version": "siesta-4.0.2",
  "E_fermi": -3.24,
  "E_fermi_units": "eV",
  "FreeE": -6656.2343
  "FreeE_units": "eV",
  "global_time": 55.213,
  "timing_decomposition": {
    "compute_DM": 33.208,
    "nlefsm-1": 0.582,
    "nlefsm-2": 0.045,
    "post-SCF": 2.556,
    "setup_H": 16.531,
    "setup_H0": 2.351,
    "siesta": 55.213,
    "state_init": 0.171
  },
  "warnings": [ "INFO: Job Completed"]
}
```
The scalar quantities to include are specified in a global-variable in the parser. Currently they are the Kohn-Sham, Free, Band, and Fermi energies, and the total spin. These are converted to ‘float’. As this dictionary is sorted, keys for program values and metadata are intermixed.

The timing information (if present), includes the global walltime in seconds, and a decomposition by sections of the code. Most relevant are typically the compute_DM and setup_H sections.

The ‘warnings’ list contains program messages, labeled as INFO, WARNING, or FATAL, read directly from a MESSAGES file produced by Siesta, which include items from the execution of the program and also a possible ‘out of time’ condition. This is implemented by passing to the program the wallclock time specified in the script, and checking at each scf step for the walltime consumed. This ‘warnings’ list can be examined by the parser itself to raise an exception in the FATAL case.
forces_and_stress ArrayData

Contains the final forces (eV/Angstrom) and stresses (GPa) in array form.
output_structure StructureData

Present only if the calculation is moving the ions. Cell and ionic positions refer to the last configuration.
bands, BandsData

Present only if a band calculation is requested (signaled by the presence of a bandskpoints input node of class KpointsData). It contains an array with the list of electronic energies (in eV) for every kpoint. For spin-polarized calculations, there is an extra dimension for spin. In this class also the full list of kpoints is stored and they are in units of 1/Angstrom. Therefore a direct comparison with the Siesta output SystLabel.bands is possible only after the conversion of Angstrom to Bohr. The bands are not rescaled by the Fermi energy. Tools for the generation of files that can be easly plot are available through bands.export.

No trajectories have been implemented yet.

Errors¶

Errors during the parsing stage are reported in the log of the calculation (accessible with the verdi process report command). Moreover, they are stored in the output_parameters node under the key warnings.

Restarts¶

A restarting capability is implemented through the optional input parent_calc_folder, RemoteData, which represents the remote scratch folder for a previous calculation.

The density-matrix file is copied from the old calculation scratch folder to the new calculation’s one.

This approach enables continuation of runs which have failed due to lack of time or insufficient convergence in the allotted number of steps.

An informative example is example_restart.py in the folder aiida_siesta/examples/plugins/siesta.

Additional advanced features¶

While the input link with name parameters is used for the main Siesta options (as would be given in an fdf file), additional settings can be specified in the settings input, also of type Dict.

Below we summarise some of the options that you can specify, and their effect.

The keys of the settings dictionary are internally converted to uppercase by the plugin.

Adding command-line options¶

If you want to add command-line options to the executable (particularly relevant e.g. to tune the parallelization level), you can pass each option as a string in a list, as follows:

settings_dict = {
    'cmdline': ['-option1', '-option2'],
}

Note that very few user-level comand-line options (besides those already inserted by AiiDA for MPI operation) are currently implemented.

Retrieving more files¶

If you know that your calculation is producing additional files that you want to retrieve (and preserve in the AiiDA repository), you can add those files as a list as follows:

settings_dict = {
  'additional_retrieve_list': ['aiida.EIG', 'aiida.ORB_INDX'],
}

See for example example_ldos.py in aiida_siesta/examples/plugins/siesta.