aiida_pythonjob_ins.workflows.dispersion

A WorkChain composing Euphonic steps into a dispersion workflow.

Steps (after force constants are resolved by ForceConstantsWorkChain – read from a CASTEP file via a PythonJob, or taken from a supplied node):

1. extract the crystal structure -> StructureData (calcfunction)

2. generate a seekpath q-point path -> KpointsData (calcfunction)

3. interpolate phonon modes on that path -> QpointPhononModesData (PythonJob)

4. compose a band structure -> BandsData (calcfunction)

The calcfunction steps run in-process (they need a loaded AiiDA profile to build nodes and are cheap), while the compute-heavy interpolate step runs as a PythonJob that could target a remote machine. Building the band path parent-side lets it return a native KpointsData directly – no custom carrier type needed. BandsData plugs into AiiDA’s plotting, e.g. results['band_structure'].show_mpl().

WorkChain reference: https://aiida.readthedocs.io/projects/aiida-core/en/stable/topics/workflows/write.html

Functions

extract_structure(→ aiida.orm.StructureData)	Extract a StructureData from any node implementing to_structure().
generate_band_path(→ aiida.orm.KpointsData)	Build a seekpath high-symmetry q-point path as a native KpointsData.
assemble_bands(→ aiida.orm.BandsData)	Compose a BandsData from phonon modes + the labelled q-point path.

Module Contents

aiida_pythonjob_ins.workflows.dispersion.extract_structure(data: aiida_pythonjob_ins.data.mixins.SupportsToStructure) → aiida.orm.StructureData

Extract a StructureData from any node implementing to_structure().

Generic on purpose: it works for ForceConstantsData, QpointPhononModesData, or any future node exposing to_structure() (the SupportsToStructure protocol). It is just a calcfunction wrapper so the method call becomes a provenance link between the input node and the StructureData on the workflow graph. AiiDA infers valid_type=(Data,) from the protocol annotation; the to_structure() call does the rest.

aiida_pythonjob_ins.workflows.dispersion.generate_band_path(structure: aiida.orm.StructureData, q_spacing: aiida.orm.Float) → aiida.orm.KpointsData

Build a seekpath high-symmetry q-point path as a native KpointsData.

Depends only on the crystal structure (seekpath needs nothing else). A parent-side calcfunction (not a PythonJob): path-building is cheap and needs a loaded profile to construct the KpointsData node, which a remote PythonJob subprocess could not do.

aiida_pythonjob_ins.workflows.dispersion.assemble_bands(modes: aiida_pythonjob_ins.data.QpointPhononModesData, qpoints: aiida.orm.KpointsData) → aiida.orm.BandsData

Compose a BandsData from phonon modes + the labelled q-point path.

A calcfunction (so the BandsData is provenance-linked) that just delegates to the Data node’s own converter; qpoints supplies the high-symmetry labels that Euphonic modes do not carry.