pymatgen.transformations.standard_transformations module¶
This module defines standard transformations which transforms a structure into another structure. Standard transformations operate in a structure-wide manner, rather than site-specific manner. All transformations should inherit the AbstractTransformation ABC.
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class
AutoOxiStateDecorationTransformation(symm_tol=0.1, max_radius=4, max_permutations=100000, distance_scale_factor=1.015)[source]¶ Bases:
pymatgen.transformations.transformation_abc.AbstractTransformationThis transformation automatically decorates a structure with oxidation states using a bond valence approach.
- Parameters
symm_tol (float) – Symmetry tolerance used to determine which sites are symmetrically equivalent. Set to 0 to turn off symmetry.
max_radius (float) – Maximum radius in Angstrom used to find nearest neighbors.
max_permutations (int) – Maximum number of permutations of oxidation states to test.
distance_scale_factor (float) – A scale factor to be applied. This is useful for scaling distances, esp in the case of calculation-relaxed structures, which may tend to under (GGA) or over bind (LDA). The default of 1.015 works for GGA. For experimental structure, set this to 1.
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class
ChargedCellTransformation(charge=0)[source]¶ Bases:
pymatgen.transformations.transformation_abc.AbstractTransformationThe ChargedCellTransformation applies a charge to a structure (or defect object).
- Parameters
charge – A integer charge to apply to the structure. Defaults to zero. Has to be a single integer. e.g. 2
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class
ConventionalCellTransformation(symprec=0.01, angle_tolerance=5, international_monoclinic=True)[source]¶ Bases:
pymatgen.transformations.transformation_abc.AbstractTransformationThis class finds the conventional cell of the input structure.
- Parameters
symprec (float) – tolerance as in SpacegroupAnalyzer
angle_tolerance (float) – angle tolerance as in SpacegroupAnalyzer
international_monoclinic (bool) – whether to use beta (True) or alpha (False)
as the non-right-angle in the unit cell
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class
DeformStructureTransformation(deformation=((1, 0, 0), (0, 1, 0), (0, 0, 1)))[source]¶ Bases:
pymatgen.transformations.transformation_abc.AbstractTransformationThis transformation deforms a structure by a deformation gradient matrix
- Parameters
deformation (array) – deformation gradient for the transformation
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class
DiscretizeOccupanciesTransformation(max_denominator=5, tol=None, fix_denominator=False)[source]¶ Bases:
pymatgen.transformations.transformation_abc.AbstractTransformationDiscretizes the site occupancies in a disordered structure; useful for grouping similar structures or as a pre-processing step for order-disorder transformations.
- Parameters
max_denominator – An integer maximum denominator for discretization. A higher denominator allows for finer resolution in the site occupancies.
tol – A float that sets the maximum difference between the original and discretized occupancies before throwing an error. If None, it is set to 1 / (4 * max_denominator).
fix_denominator (bool) – If True, will enforce a common denominator for all species. This prevents a mix of denominators (for example, 1/3, 1/4) that might require large cell sizes to perform an enumeration. ‘tol’ needs to be > 1.0 in some cases.
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class
OrderDisorderedStructureTransformation(algo=0, symmetrized_structures=False, no_oxi_states=False)[source]¶ Bases:
pymatgen.transformations.transformation_abc.AbstractTransformationOrder a disordered structure. The disordered structure must be oxidation state decorated for ewald sum to be computed. No attempt is made to perform symmetry determination to reduce the number of combinations.
Hence, attempting to performing ordering on a large number of disordered sites may be extremely expensive. The time scales approximately with the number of possible combinations. The algorithm can currently compute approximately 5,000,000 permutations per minute.
Also, simple rounding of the occupancies are performed, with no attempt made to achieve a target composition. This is usually not a problem for most ordering problems, but there can be times where rounding errors may result in structures that do not have the desired composition. This second step will be implemented in the next iteration of the code.
If multiple fractions for a single species are found for different sites, these will be treated separately if the difference is above a threshold tolerance. currently this is .1
For example, if a fraction of .25 Li is on sites 0,1,2,3 and .5 on sites 4, 5, 6, 7 then 1 site from [0,1,2,3] will be filled and 2 sites from [4,5,6,7] will be filled, even though a lower energy combination might be found by putting all lithium in sites [4,5,6,7].
USE WITH CARE.
- Parameters
algo (int) – Algorithm to use.
symmetrized_structures (bool) – Whether the input structures are instances of SymmetrizedStructure, and that their symmetry should be used for the grouping of sites.
no_oxi_states (bool) – Whether to remove oxidation states prior to ordering.
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apply_transformation(structure, return_ranked_list=False)[source]¶ For this transformation, the apply_transformation method will return only the ordered structure with the lowest Ewald energy, to be consistent with the method signature of the other transformations. However, all structures are stored in the all_structures attribute in the transformation object for easy access.
- Parameters
structure – Oxidation state decorated disordered structure to order
return_ranked_list (bool) – Whether or not multiple structures are returned. If return_ranked_list is a number, that number of structures is returned.
- Returns
Depending on returned_ranked list, either a transformed structure or a list of dictionaries, where each dictionary is of the form {“structure” = …. , “other_arguments”} the key “transformation” is reserved for the transformation that was actually applied to the structure. This transformation is parsed by the alchemy classes for generating a more specific transformation history. Any other information will be stored in the transformation_parameters dictionary in the transmuted structure class.
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class
OxidationStateDecorationTransformation(oxidation_states)[source]¶ Bases:
pymatgen.transformations.transformation_abc.AbstractTransformationThis transformation decorates a structure with oxidation states.
- Parameters
oxidation_states (dict) – Oxidation states supplied as a dict,
{"Li" (e.g.,) – 1, “O”:-2}
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class
OxidationStateRemovalTransformation[source]¶ Bases:
pymatgen.transformations.transformation_abc.AbstractTransformationThis transformation removes oxidation states from a structure.
No arg needed.
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class
PartialRemoveSpecieTransformation(specie_to_remove, fraction_to_remove, algo=0)[source]¶ Bases:
pymatgen.transformations.transformation_abc.AbstractTransformationRemove fraction of specie from a structure.
Requires an oxidation state decorated structure for ewald sum to be computed.
Given that the solution to selecting the right removals is NP-hard, there are several algorithms provided with varying degrees of accuracy and speed. Please see
pymatgen.transformations.site_transformations.PartialRemoveSitesTransformation.- Parameters
specie_to_remove – Specie to remove. Must have oxidation state E.g., “Li+”
fraction_to_remove – Fraction of specie to remove. E.g., 0.5
algo – This parameter allows you to choose the algorithm to perform ordering. Use one of PartialRemoveSpecieTransformation.ALGO_* variables to set the algo.
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apply_transformation(structure, return_ranked_list=False)[source]¶ Apply the transformation.
- Parameters
structure – input structure
return_ranked_list (bool/int) – Boolean stating whether or not multiple structures are returned. If return_ranked_list is an int, that number of structures is returned.
- Returns
Depending on returned_ranked list, either a transformed structure or a list of dictionaries, where each dictionary is of the form {“structure” = …. , “other_arguments”} the key “transformation” is reserved for the transformation that was actually applied to the structure. This transformation is parsed by the alchemy classes for generating a more specific transformation history. Any other information will be stored in the transformation_parameters dictionary in the transmuted structure class.
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class
PerturbStructureTransformation(distance=0.01, min_distance=None)[source]¶ Bases:
pymatgen.transformations.transformation_abc.AbstractTransformationThis transformation perturbs a structure by a specified distance in random directions. Used for breaking symmetries.
- Parameters
distance (float) – Distance of perturbation in angstroms. All sites will be perturbed by exactly that distance in a random direction.
min_distance (None, int, or float) – if None, all displacements will be equidistant. If int or float, perturb each site a distance drawn from the uniform distribution between ‘min_distance’ and ‘distance’.
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class
PrimitiveCellTransformation(tolerance=0.5)[source]¶ Bases:
pymatgen.transformations.transformation_abc.AbstractTransformationThis class finds the primitive cell of the input structure. It returns a structure that is not necessarily orthogonalized Author: Will Richards
- Parameters
tolerance (float) – Tolerance for each coordinate of a particular site. For example, [0.5, 0, 0.5] in cartesian coordinates will be considered to be on the same coordinates as [0, 0, 0] for a tolerance of 0.5. Defaults to 0.5.
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class
RemoveSpeciesTransformation(species_to_remove)[source]¶ Bases:
pymatgen.transformations.transformation_abc.AbstractTransformationRemove all occurrences of some species from a structure.
- Parameters
species_to_remove – List of species to remove. E.g., [“Li”, “Mn”]
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class
RotationTransformation(axis, angle, angle_in_radians=False)[source]¶ Bases:
pymatgen.transformations.transformation_abc.AbstractTransformationThe RotationTransformation applies a rotation to a structure.
- Parameters
axis (3x1 array) – Axis of rotation, e.g., [1, 0, 0]
angle (float) – Angle to rotate
angle_in_radians (bool) – Set to True if angle is supplied in radians. Else degrees are assumed.
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class
ScaleToRelaxedTransformation(unrelaxed_structure, relaxed_structure, species_map=None)[source]¶ Bases:
pymatgen.transformations.transformation_abc.AbstractTransformationTakes the unrelaxed and relaxed structure and applies its site and volume relaxation to a structurally similar structures (e.g. bulk: NaCl and PbTe (rock-salt), slab: Sc(10-10) and Mg(10-10) (hcp), GB: Mo(001) sigma 5 GB, Fe(001) sigma 5). Useful for finding an initial guess of a set of similar structures closer to its most relaxed state.
- Parameters
unrelaxed_structure (Structure) – Initial, unrelaxed structure
relaxed_structure (Structure) – Relaxed structure
species_map (dict) – A dict or list of tuples containing the species mapping in string-string pairs. The first species corresponds to the relaxed structure while the second corresponds to the species in the structure to be scaled. E.g., {“Li”:”Na”} or [(“Fe2+”,”Mn2+”)]. Multiple substitutions can be done. Overloaded to accept sp_and_occu dictionary E.g. {“Si: {“Ge”:0.75, “C”:0.25}}, which substitutes a single species with multiple species to generate a disordered structure.
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class
SubstitutionTransformation(species_map)[source]¶ Bases:
pymatgen.transformations.transformation_abc.AbstractTransformationThis transformation substitutes species for one another.
- Parameters
species_map – A dict or list of tuples containing the species mapping in string-string pairs. E.g., {“Li”:”Na”} or [(“Fe2+”,”Mn2+”)]. Multiple substitutions can be done. Overloaded to accept sp_and_occu dictionary E.g. {“Si: {“Ge”:0.75, “C”:0.25}}, which substitutes a single species with multiple species to generate a disordered structure.
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class
SupercellTransformation(scaling_matrix=((1, 0, 0), (0, 1, 0), (0, 0, 1)))[source]¶ Bases:
pymatgen.transformations.transformation_abc.AbstractTransformationThe RotationTransformation applies a rotation to a structure.
- Parameters
scaling_matrix – A matrix of transforming the lattice vectors. Defaults to the identity matrix. Has to be all integers. e.g., [[2,1,0],[0,3,0],[0,0,1]] generates a new structure with lattice vectors a” = 2a + b, b” = 3b, c” = c where a, b, and c are the lattice vectors of the original structure.
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apply_transformation(structure)[source]¶ Apply the transformation.
- Parameters
structure (Structure) – Input Structure
- Returns
Supercell Structure.
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static
from_scaling_factors(scale_a=1, scale_b=1, scale_c=1)[source]¶ Convenience method to get a SupercellTransformation from a simple series of three numbers for scaling each lattice vector. Equivalent to calling the normal with [[scale_a, 0, 0], [0, scale_b, 0], [0, 0, scale_c]]
- Parameters
scale_a – Scaling factor for lattice direction a. Defaults to 1.
scale_b – Scaling factor for lattice direction b. Defaults to 1.
scale_c – Scaling factor for lattice direction c. Defaults to 1.
- Returns
SupercellTransformation.