Sequential Converger workflow


The SiestaSequentialConverger is an iterator that sequentially runs SiestaConvergers. Once the convergence over a parameter is reached, the converged value is used for the following convergence test (on a new parameter). An example on the use of the SiestaConverger is /aiida_siesta/examples/workflows/

Supported Siesta versions

At least 4.0.1 of the 4.0 series, 4.1-b3 of the 4.1 series and the MaX-1.0 release, which can be found in the development platform ( For more up to date info on compatibility, please check the wiki.


Two are the required inputs:

  • converger_inputs, class dict, Mandatory

    A dictionary containing all the inputs required by the SiestaConverger, except the iterate_over port. The explanations of the converger inputs can be examined here <siesta-converger-inputs>. Please note that the normal inputs of a SiestaBaseWorkChain process (structure, parameters, basis, code, …) must be included as well in this dictionary.

    The same default values as SiestaConverger apply if some ports are not specified here.

  • iterate_over, class list, Mandatory

    There is a specific port for the quantities to iterate over and now the accepted value for this port is a list, not a dictionary like it was for the SiestaConverger or SiestaIterator. In fact, now the user should indicate a list of parameters that he/she wants to converge sequentially. A practical example:

        'kpoints_0': [4,10,12,14,16,18,20],
        'kpoints_1': [4,10,12,14,16,18,20],
        'kpoints_2': [4,10,12,14,16,18,20],
        'meshcutoff': ["500 Ry", "600 Ry", "700 Ry", "800 Ry", "900 Ry"],
        'pao-energyshift': ["0.02 Ry", "0.015 Ry", "0.01 Ry", "0.005 Ry", "0.001 Ry"]

    With this specification, we signal that we want to converge first the kpoints by increasing all components at the same time (assuming “zip” is selected as ‘iterate_mode’ in the converger_inputs dictionary), then the ‘meshcutoff’ and finally the ‘energy shift’. The converged kpoints will be used for the convergence of ‘meshcutoff’, the converged kpoints and ‘meshcutoff’ will be used for the convergence process of ‘energy shift’.

    Note that one can converge the same parameters again if wanted, for instance set up different rounds for kpoints convergence.


If one of the parameters does not converge, no action is taken and the following convergence step is performed using the inputs specified in converger_inputs, not using the last attempted value in the previous convergence. For instance, in the example above, if the meshcutoff does not converged at 900 Ry, the pao-energyshift convergence will be done using the inputs parameters specified in the parameters of converger_inputs, not including meshcutoff = "900 Ry".


The following outputs are returned:

  • converged_target_value Dict

    The value of the target when the convergence has been reached. Returned only if at least one of the sequential convergences has been completed succesfull.

  • converged_parameters Dict

    The values for the parameters that was enough to achieve convergence. If converged is not achieved, it will be an empty dictionary.

  • unconverged_parameters List

    If one or more parameters fail to converge, we list them in this output.

Protocol system

The protocol system is not directly available for this WorkChain. However inputs of the SiestaBaseWorkChain can be obtained in a dictionary in this way:

inp_gen = SiestaBaseWorkChain.inputs_generator()
inputs = inp_gen.get_inputs_dict(structure, calc_engines, protocols)

The inputs of get_inputs_dict are explained in the protocols documentation. Then the user can place these inputs in the converger_inputs dictionary (together with the other SiestaConverger inputs specifications). The input iterate_over is also required in order to be able to submit the SiestaSequentialConverger WorkChain and it must be set manually.