StandardConformerGenerator

pydantic model openff.qcsubmit.workflow_components.StandardConformerGenerator[source]

Standard conformer generator using the OFFTK and the back end toolkits.

Show JSON schema

{
   "title": "StandardConformerGenerator",
   "description": "Standard conformer generator using the OFFTK and the back end toolkits.",
   "type": "object",
   "properties": {
      "type": {
         "title": "Type",
         "default": "StandardConformerGenerator",
         "enum": [
            "StandardConformerGenerator"
         ],
         "type": "string"
      },
      "rms_cutoff": {
         "title": "Rms Cutoff",
         "description": "The rms cut off in angstroms to be used when generating the conformers. Passing None will use the default in toolkit of 1.",
         "type": "number"
      },
      "max_conformers": {
         "title": "Max Conformers",
         "description": "The maximum number of conformers to be generated per molecule.",
         "default": 10,
         "type": "integer"
      },
      "clear_existing": {
         "title": "Clear Existing",
         "description": "If any pre-existing conformers should be kept.",
         "default": true,
         "type": "boolean"
      }
   }
}

Config

allow_mutation: bool = True
validate_assignment: bool = True

Fields

clear_existing (bool)
max_conformers (int)
rms_cutoff (Optional[float])
type (Literal['StandardConformerGenerator'])

field type: Literal['StandardConformerGenerator'] = 'StandardConformerGenerator'

field rms_cutoff: Optional[float] = None: The rms cut off in angstroms to be used when generating the conformers. Passing None will use the default in toolkit of 1.

field max_conformers: int = 10: The maximum number of conformers to be generated per molecule.

field clear_existing: bool = True: If any pre-existing conformers should be kept.

classmethod description()[source]

Returns a friendly description of the workflow component.

Return type: str

classmethod fail_reason()[source]

Returns a friendly description of why a molecule would fail to pass the component.

Return type: str

classmethod properties()[source]

Returns the runtime properties of the component such as parallel safe.

Return type: openff.qcsubmit.common_structures.ComponentProperties

apply(molecules, toolkit_registry, processors=None, verbose=True)

This is the main feature of the workflow component which should accept a molecule, perform the component action and then return any resulting molecules.

Parameters

molecules (List[openff.toolkit.topology.molecule.Molecule]) – The list of molecules to be processed by this component.
toolkit_registry (openff.toolkit.utils.toolkit_registry.ToolkitRegistry) – The openff.toolkit.utils.ToolkitRegistry which declares the available backend toolkits to be used.
processors (Optional[int]) – The number of processor the component can use to run the job in parallel across molecules, None will default to all cores.
verbose (bool) – If true a progress bar should be shown on screen.

Returns

A component result class which handles collecting together molecules that pass and fail the component

Return type

openff.qcsubmit.workflow_components.utils.ComponentResult

classmethod info()

Returns a dictionary of the friendly descriptions of the class.

Return type: Dict[str, str]

classmethod is_available()

Check if any of the requested backend toolkits can be used.

Return type: bool

provenance(toolkit_registry)

This component calls the OFFTK to perform the task and logs information on the backend toolkit used.

Parameters: toolkit_registry (openff.toolkit.utils.toolkit_registry.ToolkitRegistry) – The openff.toolkit.utils.ToolkitRegistry which declares the available toolkits for the component.
Returns: A dictionary containing the version information about the backend toolkit called to perform the task.
Return type: Dict