pymatgen.alchemy.materials module
This module provides various representations of transformed structures. A TransformedStructure is a structure that has been modified by undergoing a series of transformations.
- class TransformedStructure(structure: Structure, transformations: list[AbstractTransformation] | None = None, history: list[AbstractTransformation | dict[str, Any]] | None = None, other_parameters: dict[str, Any] | None = None)[source]
Bases:
MSONable
Container object for new structures that include history of transformations.
Each transformed structure is made up of a sequence of structures with associated transformation history.
Initializes a transformed structure from a structure.
- Parameters:
structure (Structure) – Input structure
transformations (list[Transformation]) – List of transformations to apply.
history (list[Transformation]) – Previous history.
other_parameters (dict) – Additional parameters to be added.
- append_filter(structure_filter: AbstractStructureFilter) None [source]
Adds a filter.
- Parameters:
structure_filter (StructureFilter) – A filter implementing the AbstractStructureFilter API. Tells transmuter what structures to retain.
- append_transformation(transformation, return_alternatives=False, clear_redo=True)[source]
Appends a transformation to the TransformedStructure.
- Parameters:
transformation – Transformation to append
return_alternatives – Whether to return alternative TransformedStructures for one-to-many transformations. return_alternatives can be a number, which stipulates the total number of structures to return.
clear_redo – Boolean indicating whether to clear the redo list. By default, this is True, meaning any appends clears the history of undoing. However, when using append_transformation to do a redo, the redo list should not be cleared to allow multiple redos.
- extend_transformations(transformations: list[pymatgen.transformations.transformation_abc.AbstractTransformation], return_alternatives: bool = False) None [source]
Extends a sequence of transformations to the TransformedStructure.
- Parameters:
transformations – Sequence of Transformations
return_alternatives – Whether to return alternative TransformedStructures for one-to-many transformations. return_alternatives can be a number, which stipulates the total number of structures to return.
- static from_cif_string(cif_string: str, transformations: list[AbstractTransformation] | None = None, primitive: bool = True, occupancy_tolerance: float = 1.0) TransformedStructure [source]
Generates TransformedStructure from a cif string.
- Parameters:
cif_string (str) – Input cif string. Should contain only one structure. For cifs containing multiple structures, please use CifTransmuter.
transformations (list[Transformation]) – Sequence of transformations to be applied to the input structure.
primitive (bool) – Option to set if the primitive cell should be extracted. Defaults to True. However, there are certain instances where you might want to use a non-primitive cell, e.g., if you are trying to generate all possible orderings of partial removals or order a disordered structure.
occupancy_tolerance (float) – If total occupancy of a site is between 1 and occupancy_tolerance, the occupancies will be scaled down to 1.
- Returns:
TransformedStructure
- classmethod from_dict(d) TransformedStructure [source]
Creates a TransformedStructure from a dict.
- static from_poscar_string(poscar_string: str, transformations: list[AbstractTransformation] | None = None) TransformedStructure [source]
Generates TransformedStructure from a poscar string.
- Parameters:
poscar_string (str) – Input POSCAR string.
transformations (list[Transformation]) – Sequence of transformations to be applied to the input structure.
- classmethod from_snl(snl: StructureNL) TransformedStructure [source]
Create TransformedStructure from SNL.
- Parameters:
snl (StructureNL) – Starting snl
- Returns:
TransformedStructure
- get_vasp_input(vasp_input_set: type[pymatgen.io.vasp.sets.VaspInputSet] = <class 'pymatgen.io.vasp.sets.MPRelaxSet'>, **kwargs) dict[str, Any] [source]
Returns VASP input as a dict of vasp objects.
- Parameters:
vasp_input_set (pymatgen.io.vaspio_set.VaspInputSet) – input set to create vasp input files from structures
- redo_next_change() None [source]
Redo the last undone change in the TransformedStructure.
- Raises:
IndexError – If already at the latest change.
- set_parameter(key: str, value: Any) None [source]
Set a parameter
- Parameters:
key – The string key
value – The value.
- property structures: list[pymatgen.core.structure.Structure][source]
Copy of all structures in the TransformedStructure. A structure is stored after every single transformation.
- to_snl(authors, **kwargs) StructureNL [source]
Generate SNL from TransformedStructure.
- Parameters:
authors – List of authors
**kwargs –
All kwargs supported by StructureNL.
- Returns:
StructureNL
- undo_last_change() None [source]
Undo the last change in the TransformedStructure.
- Raises:
IndexError – If already at the oldest change.
- property was_modified: bool[source]
Boolean describing whether the last transformation on the structure made any alterations to it one example of when this would return false is in the case of performing a substitution transformation on the structure when the specie to replace isn’t in the structure.
- write_vasp_input(vasp_input_set: type[pymatgen.io.vasp.sets.VaspInputSet] = <class 'pymatgen.io.vasp.sets.MPRelaxSet'>, output_dir: str = '.', create_directory: bool = True, **kwargs) None [source]
Writes VASP input to an output_dir.
- Parameters:
vasp_input_set – pymatgen.io.vaspio_set.VaspInputSet like object that creates vasp input files from structures.
output_dir – Directory to output files
create_directory – Create the directory if not present. Defaults to True.
**kwargs – All keyword args supported by the VASP input set.