Dissociation curve

The Dissociation class allows to perform all the jobs necessary to obtain the dissociation curve between two fragments (sub- systems).

class mybigdft.workflows.dissociation.Dissociation(fragment1, fragment2, distances, inputparams=None, name='', run_dir=None)[source]

Bases: mybigdft.workflows.workflow.AbstractWorkflow

This workflow allows to get all the necessary data in order to plot a dissociation curve of two fragments (or sub-systems). This curve represent how the energy of the whole system is modified when the distance between the two fragments varies.

The fragments will be separated from each other in the \(y\) direction according to a set of distances.

A queue of Job instances is initialized, one per distance between the fragments. For each job, the system is made of both fragments, where each atom of the second fragment is translated along the \(y\) direction by the value of the distance.

Warning

If both fragments do not have the same boundary conditions or cell, it is the ones of the first fragment that are used.

Note

The \(y\) axis was chosen so that surface boundary conditions could be used as well (for instance, the first fragment can be a surface while the second is an atom or molecule).

Parameters

  • inputparams (InputParams) –

  • fragment1 (Posinp) – Posinp of the first fragment.

  • fragment2 (Posinp) – Posinp of the second fragment.

  • distances (list or numpy.array) – Distances between both fragments.

  • name (str) – Name to be used for each job.

Raises

ValueError – If one fragment defines periodic boundary conditions.

property minimum
Returns

Job that gave the lowest energy.

Return type

Job

property energies
Returns

Total energy of each job in the queue.

Return type

list

post_proc()[source]

Find the energy of each job and the job that gave the minimum energy.

property is_completed
Returns

True if all the post-processing attributes are no longer set to their default value.

Return type

bool

property logfiles
Returns

A dictionary of all the logfiles of the workflow, with the name of the associated job as key.

Return type

dict

property queue
Returns

All the jobs of the workflow.

Return type

list

run(nmpi=1, nomp=1, force_run=False, dry_run=False, restart_if_incomplete=False, timeout=None)

Run all the calculations if the post-processing was not already performed.

Warning

If force_run or dry_run is set to True, then any previous value of the post-processing attributes is deleted and set back to their default value, so that the post-processing is not considered as being performed.

Parameters

  • nmpi (int) – Number of MPI tasks.

  • nomp (int) – Number of OpenMP tasks.

  • force_run (bool) – If True, the calculations are run even though a logfile already exists.

  • dry_run (bool) – If True, the input files are written on disk, but the bigdft-tool command is run instead of the bigdft one.

  • restart_if_incomplete (bool) – If True, the job is restarted if the existing logfile is incomplete.

  • timeout (float or int or None) – Number of minutes after which each job must be stopped.

Warns

UserWarning – If the post-processing was already completed.