pymatgen.transformations package

The transformations package defines various transformations that can be applied on structures, i.e., converting one structure to another.

Submodules

pymatgen.transformations.advanced_transformations module

This module implements more advanced transformations.

class AddAdsorbateTransformation(adsorbate, selective_dynamics=False, height=0.9, mi_vec=None, repeat=None, min_lw=5.0, translate=True, reorient=True, find_args=None)[source]

Bases: AbstractTransformation

Create adsorbate structures.

Use AdsorbateSiteFinder to add an adsorbate to a slab.

Parameters:

adsorbate (Molecule) – molecule to add as adsorbate
selective_dynamics (bool) – flag for whether to assign non-surface sites as fixed for selective dynamics
height (float) – height criteria for selection of surface sites
mi_vec – vector corresponding to the vector concurrent with the miller index, this enables use with slabs that have been reoriented, but the miller vector must be supplied manually
repeat (3-tuple or list) – repeat argument for supercell generation
min_lw (float) – minimum length and width of the slab, only used if repeat is None
translate (bool) – flag on whether to translate the molecule so that its CoM is at the origin prior to adding it to the surface
reorient (bool) – flag on whether or not to reorient adsorbate along the miller index
find_args (dict) – dictionary of arguments to be passed to the call to self.find_adsorption_sites, e.g. {“distance”:2.0}

apply_transformation(structure: Structure, return_ranked_list: bool | int = False)[source]

Parameters:

structure – Must be a Slab structure
return_ranked_list (bool | int, optional) –
If return_ranked_list is int, that number of structures.

is returned. If False, only the single lowest energy structure is returned. Defaults to False.

Returns:

with adsorbate

Return type:

Slab

property is_one_to_many: Literal[True][source]: Transform one structure to many.

class ChargeBalanceTransformation(charge_balance_sp)[source]

Bases: AbstractTransformation

This is a transformation that disorders a structure to make it charge balanced, given an oxidation state-decorated structure.

Parameters:: charge_balance_sp – specie to add or remove. Currently only removal is supported.

apply_transformation(structure: Structure)[source]

Apply the transformation.

Parameters:: structure – Input Structure
Returns:: Charge balanced structure.

class CubicSupercellTransformation(min_atoms: int | None = None, max_atoms: int | None = None, min_length: float = 15.0, max_length: float | None = None, force_diagonal: bool = False, force_90_degrees: bool = False, allow_orthorhombic: bool = False, angle_tolerance: float = 0.001, step_size: float = 0.1)[source]

Bases: AbstractTransformation

A transformation that aims to generate a nearly cubic supercell structure from a structure.

The algorithm solves for a transformation matrix that makes the supercell cubic. The matrix must have integer entries, so entries are rounded (in such a way that forces the matrix to be non-singular). From the supercell resulting from this transformation matrix, vector projections are used to determine the side length of the largest cube that can fit inside the supercell. The algorithm will iteratively increase the size of the supercell until the largest inscribed cube’s side length is at least ‘min_length’ and the number of atoms in the supercell falls in the range min_atoms < n < max_atoms.

Parameters:

max_atoms – Maximum number of atoms allowed in the supercell.
min_atoms – Minimum number of atoms allowed in the supercell.
min_length – Minimum length of the smallest supercell lattice vector.
max_length – Maximum length of the larger supercell lattice vector.
force_diagonal – If True, return a transformation with a diagonal transformation matrix.
force_90_degrees – If True, return a transformation for a supercell with 90 degree angles (if possible). To avoid long run times, please use max_atoms or max_length
allow_orthorhombic – Instead of a cubic cell, also orthorhombic cells are allowed. max_length is required for this option.
angle_tolerance – tolerance to determine the 90 degree angles.
step_size (float) – step_size which is used to increase the supercell. If allow_orthorhombic and force_90_degrees is both set to True, the chosen step_size will be automatically multiplied by 5 to prevent a too long search for the possible supercell.

apply_transformation(structure: Structure) → Structure[source]

The algorithm solves for a transformation matrix that makes the supercell cubic. The matrix must have integer entries, so entries are rounded (in such a way that forces the matrix to be non-singular). From the supercell resulting from this transformation matrix, vector projections are used to determine the side length of the largest cube that can fit inside the supercell. The algorithm will iteratively increase the size of the supercell until the largest inscribed cube’s side length is at least ‘num_nn_dists’ times the nearest neighbor distance and the number of atoms in the supercell falls in the range defined by min_atoms and max_atoms.

Returns:: Transformed supercell.
Return type:: supercell

check_constraints(length_vecs, n_atoms, superstructure)[source]

Check if the supercell constraints are met.

Returns:: bool

check_exceptions(length_vecs, n_atoms)[source]: Check supercell exceptions.

static get_possible_supercell(lat_vecs, structure, target_sc_lat_vecs)[source]

Get the supercell possible with the set conditions.

Returns:: length_vecs, n_atoms, superstructure, transformation_matrix

class DisorderOrderedTransformation(max_sites_to_merge=2)[source]

Bases: AbstractTransformation

Not to be confused with OrderDisorderedTransformation, this transformation attempts to obtain a disordered structure from an input ordered structure. This may or may not be physically plausible, further inspection of the returned structures is advised. The main purpose for this transformation is for structure matching to crystal prototypes for structures that have been derived from a parent prototype structure by substitutions or alloying additions.

Parameters:: max_sites_to_merge – only merge this number of sites together.

apply_transformation(structure: Structure, return_ranked_list: bool | int = False)[source]

Parameters:

structure – ordered structure
return_ranked_list (bool | int, optional) –
If return_ranked_list is int, that number of structures.

is returned. If False, only the single lowest energy structure is returned. Defaults to False.

Returns:

Transformed disordered structure(s)

property is_one_to_many: Literal[True][source]: Transform one structure to many.

class DopingTransformation(dopant, ionic_radius_tol=inf, min_length=10, alio_tol=0, codopant=False, max_structures_per_enum=100, allowed_doping_species=None, **kwargs)[source]

Bases: AbstractTransformation

A transformation that performs doping of a structure.

Parameters:

dopant (Species-like) – e.g. Al3+. Must have oxidation state.
ionic_radius_tol (float) – e.g. Fractional allowable ionic radii mismatch for dopant to fit into a site. Default of inf means that any dopant with the right oxidation state is allowed.
min_length (float) – Min. lattice parameter between periodic images of dopant. Defaults to 10A for now.
alio_tol (int) – If this is not 0, attempt will be made to dope sites with oxidation_states +- alio_tol of the dopant. e.g. 1 means that the ions like Ca2+ and Ti4+ are considered as potential doping sites for Al3+.
codopant (bool) – If True, doping will be carried out with a codopant to maintain charge neutrality. Otherwise, vacancies will be used.
max_structures_per_enum (float) – Maximum number of structures to return per enumeration. Note that there can be more than one candidate doping site, and each site enumeration will return at max max_structures_per_enum structures. Defaults to 100.
allowed_doping_species (list) – Species that are allowed to be doping sites. This is an inclusionary list. If specified, any sites which are not
**kwargs – Same keyword args as EnumerateStructureTransformation, i.e., min_cell_size, etc.

apply_transformation(structure: Structure, return_ranked_list: bool | int = False) → list[dict[str, Any]] | Structure[source]

Parameters:

structure (Structure) – Input structure to dope.
return_ranked_list (bool | int, optional) – If is int, that number of structures is returned. If False, only the single lowest energy structure is returned. Defaults to False.

Returns:

each dict as {“structure”: Structure, “energy”: float}.

Return type:

list[dict] | Structure

property is_one_to_many: Literal[True][source]: Transform one structure to many.

class EnumerateStructureTransformation(min_cell_size: int = 1, max_cell_size: int = 1, symm_prec: float = 0.1, refine_structure: bool = False, enum_precision_parameter: float = 0.001, check_ordered_symmetry: bool = True, max_disordered_sites: int | None = None, sort_criteria: str | Callable = 'ewald', timeout: float | None = None, n_jobs: int = -1)[source]

Bases: AbstractTransformation

Order a disordered structure using enumlib. For complete orderings, this generally produces fewer structures that the OrderDisorderedStructure transformation, and at a much faster speed.

Parameters:

min_cell_size – The minimum cell size wanted. Must be an int. Defaults to 1.
max_cell_size – The maximum cell size wanted. Must be an int. Defaults to 1.
symm_prec – Tolerance to use for symmetry.
refine_structure – This parameter has the same meaning as in enumlib_caller. If you are starting from a structure that has been relaxed via some electronic structure code, it is usually much better to start with symmetry determination and then obtain a refined structure. The refined structure have cell parameters and atomic positions shifted to the expected symmetry positions, which makes it much less sensitive precision issues in enumlib. If you are already starting from an experimental cif, refinement should have already been done and it is not necessary. Defaults to False.
enum_precision_parameter (float) – Finite precision parameter for enumlib. Default of 0.001 is usually ok, but you might need to tweak it for certain cells.
check_ordered_symmetry (bool) – Whether to check the symmetry of the ordered sites. If the symmetry of the ordered sites is lower, the lowest symmetry ordered sites is included in the enumeration. This is important if the ordered sites break symmetry in a way that is important getting possible structures. But sometimes including ordered sites slows down enumeration to the point that it cannot be completed. Switch to False in those cases. Defaults to True.
max_disordered_sites (int) – An alternate parameter to max_cell size. Will sequentially try larger and larger cell sizes until (i) getting a result or (ii) the number of disordered sites in the cell exceeds max_disordered_sites. Must set max_cell_size to None when using this parameter.
sort_criteria (str or callable) – Sort by Ewald energy (“ewald”, must have oxidation states and slow) or M3GNet relaxed energy (“m3gnet_relax”, which is the most accurate but most expensive and provides pre-relaxed structures - needs m3gnet package installed) or by M3GNet static energy (“m3gnetassets”) or by number of sites (“nsites”, the fastest, the default). The expense of m3gnet_relax or m3gnetassets can be worth it if it significantly reduces the number of structures to be considered. m3gnet_relax speeds up the subsequent DFT calculations. Alternatively, a callable can be supplied that returns a (Structure, energy) tuple.
timeout (float) – timeout in minutes to pass to EnumlibAdaptor.
n_jobs (int) – Number of parallel jobs used to compute energy criteria. This is used only when the Ewald or m3gnet or callable sort_criteria is used. Default is -1, which uses all available CPUs.

apply_transformation(structure: Structure, return_ranked_list: bool | int = False) → Structure | list[dict][source]

Get either a single ordered structure or a sequence of all ordered structures.

Parameters:

structure – Structure to order.
return_ranked_list (bool | int, optional) –
If return_ranked_list is int, that number of structures

is returned. If False, only the single lowest energy structure is returned. Defaults to False.

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 list of ordered structures is ranked by Ewald energy / atom, if the input structure is an oxidation state decorated structure. Otherwise, it is ranked by number of sites, with smallest number of sites first.

property is_one_to_many: Literal[True][source]: Transform one structure to many.

class GrainBoundaryTransformation(rotation_axis, rotation_angle, expand_times=4, vacuum_thickness=0.0, ab_shift: tuple[float, float] | None = None, normal=False, ratio=True, plane=None, max_search=20, tol_coi=1e-08, rm_ratio=0.7, quick_gen=False)[source]

Bases: AbstractTransformation

A transformation that creates a gb from a bulk structure.

Parameters:

rotation_axis (list) – Rotation axis of GB in the form of a list of integer e.g.: [1, 1, 0]
rotation_angle (float, in unit of degree) – rotation angle used to generate GB. Make sure the angle is accurate enough. You can use the enum* functions in this class to extract the accurate angle. e.g.: The rotation angle of sigma 3 twist GB with the rotation axis [1, 1, 1] and GB plane (1, 1, 1) can be 60.000000000 degree. If you do not know the rotation angle, but know the sigma value, we have provide the function get_rotation_angle_from_sigma which is able to return all the rotation angles of sigma value you provided.
expand_times (int) – The multiple times used to expand one unit grain to larger grain. This is used to tune the grain length of GB to warrant that the two GBs in one cell do not interact with each other. Default set to 4.
vacuum_thickness (float) – The thickness of vacuum that you want to insert between two grains of the GB. Default to 0.
ab_shift (tuple[float, float]) – in plane shift of two grains in unit of a, b vectors of Gb
normal (logic) – determine if need to require the c axis of top grain (first transformation matrix) perpendicular to the surface or not. default to false.
ratio (list of integers) –
lattice axial ratio. If True, will try to determine automatically from structure. For cubic system, ratio is not needed and can be set to None. For tetragonal system, ratio = [mu, mv], list of two integers, that is, mu/mv = c2/a2. If it is irrational, set it to None. For orthorhombic system, ratio = [mu, lam, mv], list of three integers,

that is, mu:lam:mv = c2:b2:a2. If irrational for one axis, set it to None.

e.g. mu:lam:mv = c2,None,a2, means b2 is irrational. For rhombohedral system, ratio = [mu, mv], list of two integers, that is, mu/mv is the ratio of (1+2*cos(alpha))/cos(alpha). If irrational, set it to None. For hexagonal system, ratio = [mu, mv], list of two integers, that is, mu/mv = c2/a2. If it is irrational, set it to none.
plane (list) – Grain boundary plane in the form of a list of integers e.g.: [1, 2, 3]. If none, we set it as twist GB. The plane will be perpendicular to the rotation axis.
max_search (int) – max search for the GB lattice vectors that give the smallest GB lattice. If normal is true, also max search the GB c vector that perpendicular to the plane. For complex GB, if you want to speed up, you can reduce this value. But too small of this value may lead to error.
tol_coi (float) – tolerance to find the coincidence sites. When making approximations to the ratio needed to generate the GB, you probably need to increase this tolerance to obtain the correct number of coincidence sites. To check the number of coincidence sites are correct or not, you can compare the generated Gb object’s sigma with enum* sigma values (what user expected by input).
rm_ratio (float) – the criteria to remove the atoms which are too close with each other. rm_ratio * bond_length of bulk system is the criteria of bond length, below which the atom will be removed. Default to 0.7.
quick_gen (bool) – whether to quickly generate a supercell, if set to true, no need to find the smallest cell.

apply_transformation(structure: Structure)[source]

Apply the transformation.

Parameters:

structure – Input Structure
return_ranked_list (bool | int, optional) – If return_ranked_list is int, that number of structures is returned. If False, only the single lowest energy structure is returned. Defaults to False.

Returns:

Grain boundary Structures.

class MagOrderParameterConstraint(order_parameter, species_constraints=None, site_constraint_name=None, site_constraints=None)[source]

Bases: MSONable

This class can be used to supply MagOrderingTransformation to just a specific subset of species or sites that satisfy the provided constraints. This can be useful for setting an order parameters for, for example, ferrimagnetic structures which might order on certain motifs, with the global order parameter dependent on how many sites satisfy that motif.

Parameters:

order_parameter (float) – any number from 0.0 to 1.0, typically 0.5 (antiferromagnetic) or 1.0 (ferromagnetic)
species_constraints (list) – str or list of strings of Species symbols that the constraint should apply to
site_constraint_name (str) – name of the site property that the constraint should apply to, e.g. “coordination_no”
site_constraints (list) – list of values of the site property that the constraints should apply to.

satisfies_constraint(site)[source]: Check if a periodic site satisfies the constraint.

class MagOrderingTransformation(mag_species_spin, order_parameter=0.5, energy_model=None, **kwargs)[source]

Bases: AbstractTransformation

This transformation takes a structure and returns a list of collinear magnetic orderings. For disordered structures, make an ordered approximation first.

Parameters:

mag_species_spin – A mapping of elements/species to their spin magnitudes, e.g. {“Fe3+”: 5, “Mn3+”: 4}
order_parameter (float or list) – if float, a specifies a global order parameter and can take values from 0.0 to 1.0 (e.g. 0.5 for antiferromagnetic or 1.0 for ferromagnetic), if list has to be a list of pymatgen.transformations.advanced_transformations.MagOrderParameterConstraint to specify more complicated orderings, see documentation for MagOrderParameterConstraint more details on usage
energy_model – Energy model to rank the returned structures, see :mod: pymatgen.analysis.energy_models for more information (note that this is not necessarily a physical energy). By default, returned structures use SymmetryModel() which ranks structures from most symmetric to least.
kwargs – Additional kwargs that are passed to EnumerateStructureTransformation such as min_cell_size etc.

apply_transformation(structure: Structure, return_ranked_list: bool | int = False) → Structure | list[Structure][source]

Apply MagOrderTransformation to an input structure.

Parameters:

structure (Structure) – Any ordered structure.
return_ranked_list (bool | int, optional) – If return_ranked_list is int, that number of structures is returned. If False, only the single lowest energy structure is returned. Defaults to False.

Raises:

ValueError – On disordered structures.

Returns:

Structure(s) after MagOrderTransformation.

Return type:

Structure | list[Structure]

static determine_min_cell(disordered_structure)[source]: Determine the smallest supercell that is able to enumerate the provided structure with the given order parameter.

property is_one_to_many: Literal[True][source]: Transform one structure to many.

class MonteCarloRattleTransformation(rattle_std: float, min_distance: float, seed: int | None = None, **kwargs)[source]

Bases: AbstractTransformation

Uses a Monte Carlo rattle procedure to randomly perturb the sites in a structure.

This class requires the hiPhive package to be installed.

Rattling atom i is carried out as a Monte Carlo move that is accepted with a probability determined from the minimum interatomic distance \(d_{ij}\). If \(\\min(d_{ij})\) is smaller than \(d_{min}\) the move is only accepted with a low probability.

This process is repeated for each atom a number of times meaning the magnitude of the final displacements is not directly connected to rattle_std.

Parameters:

rattle_std – Rattle amplitude (standard deviation in normal distribution). Note: this value is not directly connected to the final average displacement for the structures
min_distance – Interatomic distance used for computing the probability for each rattle move.
seed – Seed for setting up NumPy random state from which random numbers are generated. If None, a random seed will be generated (default). This option allows the output of this transformation to be deterministic.
**kwargs – Additional keyword arguments to be passed to the hiPhive mc_rattle function.

apply_transformation(structure: Structure) → Structure[source]

Apply the transformation.

Parameters:: structure – Input Structure
Returns:: Structure with sites perturbed.

class MultipleSubstitutionTransformation(sp_to_replace, r_fraction, substitution_dict, charge_balance_species=None, order=True)[source]

Bases: object

Perform multiple substitutions on a structure. For example, can do a fractional replacement of Ge in LiGePS with a list of species, creating one structure for each substitution. Ordering is done using a dummy element so only one ordering must be done per substitution oxidation state. Charge balancing of the structure is optionally performed.

Note

There are no checks to make sure that removal fractions are possible and rounding may occur. Currently charge balancing only works for removal of species.

Perform multiple fractional substitutions on a transmuter.

Parameters:

sp_to_replace – species to be replaced
r_fraction – fraction of that specie to replace
substitution_dict – dictionary of the format {2: [“Mg”, “Ti”, “V”, “As”, “Cr”, “Ta”, “N”, “Nb”], 3: [“Ru”, “Fe”, “Co”, “Ce”, “As”, “Cr”, “Ta”, “N”, “Nb”], 4: [“Ru”, “V”, “Cr”, “Ta”, “N”, “Nb”], 5: [“Ru”, “W”, “Mn”] } The number is the charge used for each of the list of elements (an element can be present in multiple lists)
charge_balance_species – If specified, will balance the charge on the structure using that specie.
order – Whether to order the structures.

apply_transformation(structure: Structure, return_ranked_list: bool | int = False)[source]

Apply the transformation.

Parameters:

structure – Input Structure
return_ranked_list (bool | int, optional) –
If return_ranked_list is int, that number of structures

is returned. If False, only the single lowest energy structure is returned. Defaults to False.

Returns:

Structures with all substitutions applied.

property is_one_to_many: Literal[True][source]: Transform one structure to many.

class SQSTransformation(scaling: int | list[int], cluster_size_and_shell: dict[int, int] | None = None, search_time: float = 60, directory: str | None = None, instances: int | None = None, temperature: float = 1, wr: float = 1, wn: float = 1, wd: float = 0.5, tol: float = 0.001, icet_sqs_kwargs: dict[str, Any] | None = None, best_only: bool = True, remove_duplicate_structures: bool = True, reduction_algo: Literal['niggli', 'LLL'] = 'LLL', sqs_method: Literal['mcsqs', 'icet-enumeration', 'icet-monte_carlo'] = 'mcsqs')[source]

Bases: AbstractTransformation

A transformation that creates a special quasi-random structure (SQS) from a structure with partial occupancies.

Parameters:

scaling (int or list) –
Scaling factor to determine supercell. Two options are possible: a. (preferred) Scales number of atoms, e.g. for a structure with 8 atoms,

scaling=4 would lead to a 32 atom supercell
1. A sequence of three scaling factors, e.g. [2, 1, 1], which
  specifies that the supercell should have dimensions 2a x b x c
cluster_size_and_shell (Optional[Dict[int, int]]) – Dictionary of cluster interactions with entries in the form number of atoms: nearest neighbor shell
search_time (float, optional) – If sqs_method == “mcsqs”, the time spent looking for the ideal SQS in minutes (default: 60)
directory (str, optional) – Directory to run mcsqs calculation and store files (default: None runs calculations in a temp directory)
instances (int, optional) – Specifies the number of parallel instances of mcsqs to run (default: number of cpu cores detected by Python)
temperature (float, optional) – Monte Carlo temperature (default: 1), “T” in atat code
wr (float, optional) – Weight assigned to range of perfect correlation match in objective function (default = 1)
wn (float, optional) – Multiplicative decrease in weight per additional point in cluster (default: 1)
wd (float, optional) – Exponent of decay in weight as function of cluster diameter (default: 0)
tol (float, optional) – Tolerance for matching correlations (default: 1e-3)
icet_sqs_kwargs (dict) – If icet is used for the SQS search, kwargs to pass to pymatgen.io.icet.IcetSQS
best_only (bool, optional) – only return structures with lowest objective function
remove_duplicate_structures (bool, optional) – only return unique structures
reduction_algo (str, optional) – The lattice reduction algorithm to use. One of “niggli” or “LLL”. Passing False does not reduce structure.
sqs_method (str) – One of “mcsqs” (MCSQS method from ATAT), “icet-enumeration” (enumeration of all possible SQS structures of a given size with icet), or “icet-monte_carlo” (Monte Carlo search with icet, similar to MCSQS).

apply_transformation(structure: Structure, return_ranked_list: bool | int = False)[source]

Apply SQS transformation.

Parameters:

structure (pymatgen Structure) – pymatgen Structure with partial occupancies
return_ranked_list (bool | int, optional) –
If return_ranked_list is int, that number of structures

is returned. If False, only the single lowest energy structure is returned. Defaults to False.

Returns:

pymatgen Structure which is an SQS of the input structure

property is_one_to_many: Literal[True][source]: Transform one structure to many.

class SlabTransformation(miller_index, min_slab_size, min_vacuum_size, lll_reduce=False, center_slab=False, in_unit_planes=False, primitive=True, max_normal_search=None, shift=0, tol=0.1)[source]

Bases: AbstractTransformation

A transformation that creates a slab from a structure.

Parameters:

miller_index (3-tuple or list) – miller index of slab
min_slab_size (float) – minimum slab size in angstroms
min_vacuum_size (float) – minimum size of vacuum
lll_reduce (bool) – whether to apply LLL reduction
center_slab (bool) – whether to center the slab
primitive (bool) – whether to reduce slabs to most primitive cell
in_unit_planes (bool) – Whether to set min_slab_size and min_vac_size in units of hkl planes (True) or Angstrom (False, the default). Setting in units of planes is useful for ensuring some slabs have a certain n_layer of atoms. e.g. for Cs (100), a 10 Ang slab will result in a slab with only 2 layer of atoms, whereas Fe (100) will have more layer of atoms. By using units of hkl planes instead, we ensure both slabs have the same number of atoms. The slab thickness will be in min_slab_size/math.ceil(self._proj_height/dhkl) multiples of oriented unit cells.
max_normal_search (int) – maximum index to include in linear combinations of indices to find c lattice vector orthogonal to slab surface
shift (float) – shift to get termination
tol (float) – tolerance for primitive cell finding.

apply_transformation(structure: Structure)[source]

Apply the transformation.

Parameters:: structure – Input Structure
Returns:: Slab Structures.

class SubstituteSurfaceSiteTransformation(atom, selective_dynamics=False, height=0.9, mi_vec=None, target_species=None, sub_both_sides=False, range_tol=0.01, dist_from_surf=0)[source]

Bases: AbstractTransformation

Use AdsorptionSiteFinder to perform substitution-type doping on the surface and returns all possible configurations where one dopant is substituted per surface. Can substitute one surface or both.

Parameters:

atom (str) – atom corresponding to substitutional dopant
selective_dynamics (bool) – flag for whether to assign non-surface sites as fixed for selective dynamics
height (float) – height criteria for selection of surface sites
mi_vec – vector corresponding to the vector concurrent with the miller index, this enables use with slabs that have been reoriented, but the miller vector must be supplied manually
target_species – List of specific species to substitute
sub_both_sides (bool) – If true, substitute an equivalent site on the other surface
range_tol (float) – Find viable substitution sites at a specific distance from the surface +- this tolerance
dist_from_surf (float) – Distance from the surface to find viable substitution sites, defaults to 0 to substitute at the surface.

apply_transformation(structure: Structure, return_ranked_list: bool | int = False) → list[dict] | Structure[source]

Parameters:

structure – Must be a Slab structure
return_ranked_list (bool | int, optional) –
If return_ranked_list is int, that number of structures.

is returned. If False, only the single lowest energy structure is returned. Defaults to False.

Returns:

each dict has key ‘structure’ which is a Slab with sites substituted

Return type:

list[dict]

property is_one_to_many: Literal[True][source]: Transform one structure to many.

class SubstitutionPredictorTransformation(threshold=0.01, scale_volumes=True, **kwargs)[source]

Bases: AbstractTransformation

This transformation takes a structure and uses the structure prediction module to find likely site substitutions.

Parameters:

threshold – Threshold for substitution.
scale_volumes – Whether to scale volumes after substitution.
**kwargs – Args for SubstitutionProbability class lambda_table, alpha.

apply_transformation(structure: Structure, return_ranked_list: bool | int = False)[source]

Apply the transformation.

Parameters:

structure – Input Structure
return_ranked_list (bool | int, optional) –
If return_ranked_list is int, that number of structures

is returned. If False, only the single lowest energy structure is returned. Defaults to False.

Returns:

Predicted Structures.

property is_one_to_many: Literal[True][source]: Transform one structure to many.

class SuperTransformation(transformations, nstructures_per_trans=1)[source]

Bases: AbstractTransformation

This is a transformation that is inherently one-to-many. It is constructed from a list of transformations and returns one structure for each transformation. The primary use for this class is extending a transmuter object.

Parameters:

transformations ([transformations]) – List of transformations to apply to a structure. One transformation is applied to each output structure.
nstructures_per_trans (int) – If the transformations are one-to-many and, nstructures_per_trans structures from each transformation are added to the full list. Defaults to 1, i.e., only best structure.

apply_transformation(structure: Structure, return_ranked_list: bool | int = False)[source]

Apply the transformation.

Parameters:

structure – Input Structure
return_ranked_list (bool | int, optional) –
If return_ranked_list is int, that number of structures

is returned. If False, only the single lowest energy structure is returned. Defaults to False.

Returns:

Structures with all transformations applied.

property is_one_to_many: Literal[True][source]: Transform one structure to many.

find_codopant(target: Species, oxidation_state: float, allowed_elements: Sequence[str] | None = None) → Species[source]

Find the element from “allowed elements” that (i) possesses the desired “oxidation state” and (ii) is closest in ionic radius to the target specie.

Parameters:

target (Species) – provides target ionic radius.
oxidation_state (float) – co-dopant oxidation state.
allowed_elements (list[str]) – List of allowed elements. If None, all elements are tried.

Returns:

with oxidation_state that has ionic radius closest to target.

Return type:

Species

pymatgen.transformations.site_transformations module

This module defines site transformations which transforms a structure into another structure. Site transformations differ from standard transformations in that they operate in a site-specific manner. All transformations should inherit the AbstractTransformation ABC.

class AddSitePropertyTransformation(site_properties)[source]

Bases: AbstractTransformation

Simple transformation to add site properties to a given structure.

Parameters:: site_properties (dict) – site properties to be added to a structure.

apply_transformation(structure: Structure)[source]

Apply the transformation.

Parameters:: structure (Structure) – A structurally similar structure in regards to crystal and site positions.
Returns:: A copy of structure with sites properties added.

class InsertSitesTransformation(species, coords, coords_are_cartesian=False, validate_proximity=True)[source]

Bases: AbstractTransformation

This transformation substitutes certain sites with certain species.

Parameters:

species – A list of species. e.g. [“Li”, “Fe”]
coords – A list of coords corresponding to those species. e.g. [[0,0,0],[0.5,0.5,0.5]].
coords_are_cartesian (bool) – Set to True if coords are given in Cartesian coords. Defaults to False.
validate_proximity (bool) – Set to False if you do not wish to ensure that added sites are not too close to other sites. Defaults to True.

apply_transformation(structure: Structure)[source]

Apply the transformation.

Parameters:: structure (Structure) – A structurally similar structure in regards to crystal and site positions.
Returns:: A copy of structure with sites inserted.

class PartialRemoveSitesTransformation(indices, fractions, algo=1)[source]

Bases: AbstractTransformation

Remove 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. The options are as follows:

ALGO_FAST:: This is a highly optimized algorithm to quickly go through the search tree. It is guaranteed to find the optimal solution, but will return only a single lowest energy structure. Typically, you will want to use this.
ALGO_COMPLETE:: The complete algo ensures that you get all symmetrically distinct orderings, ranked by the estimated Ewald energy. But this can be an extremely time-consuming process if the number of possible orderings is very large. Use this if you really want all possible orderings. If you want just the lowest energy ordering, ALGO_FAST is accurate and faster.
ALGO_BEST_FIRST:: This algorithm is for ordering the really large cells that defeats even ALGO_FAST. For example, if you have 48 sites of which you want to remove 16 of them, the number of possible orderings is around 2 x 10^12. ALGO_BEST_FIRST shortcircuits the entire search tree by removing the highest energy site first, then followed by the next highest energy site, and so on. It is guaranteed to find a solution in a reasonable time, but it is also likely to be highly inaccurate.
ALGO_ENUMERATE:: This algorithm uses the EnumerateStructureTransformation to perform ordering. This algo returns complete orderings up to a single unit cell size. It is more robust than the ALGO_COMPLETE, but requires Gus Hart’s enumlib to be installed.

Parameters:

indices – A list of list of indices, e.g. [[0, 1], [2, 3, 4, 5]].
fractions – The corresponding fractions to remove. Must be same length as indices. e.g. [0.5, 0.25]
algo – This parameter allows you to choose the algorithm to perform ordering. Use one of PartialRemoveSpecieTransformation.ALGO_* variables to set the algo.

ALGO_BEST_FIRST = 2[source]

ALGO_COMPLETE = 1[source]

ALGO_ENUMERATE = 3[source]

ALGO_FAST = 0[source]

apply_transformation(structure: Structure, return_ranked_list: bool | int = False)[source]

Apply the transformation.

Parameters:

structure – input structure
return_ranked_list (bool | int) – Whether or not multiple structures are returned. If return_ranked_list is 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.

property is_one_to_many: bool[source]: Transform one structure to many.

class RadialSiteDistortionTransformation(site_index: int, displacement: float = 0.1, nn_only: bool = False)[source]

Bases: AbstractTransformation

Radially perturbs atoms around a site. Can be used to create spherical distortion due to a point defect.

Parameters:

site_index (int) – index of the site in structure to place at the center of the distortion (will not be distorted). This index must be provided before the structure is provided in apply_transformation in order to keep in line with the base class.
displacement (float) – distance to perturb the atoms around the objective site
nn_only (bool) – Whether to perturb beyond the nearest neighbors. If True, then only the nearest neighbors will be perturbed, leaving the other sites undisturbed. If False, then the nearest neighbors will receive the full displacement, and then subsequent sites will receive a displacement=0.1 / r, where r is the distance each site to the origin site. For small displacements, atoms beyond the NN environment will receive very small displacements, and these are almost equal. For large displacements, this difference is noticeable.

apply_transformation(structure: Structure)[source]

Apply the transformation.

Parameters:: structure – Structure or Molecule to apply the transformation to
Returns:: the transformed structure

property is_one_to_many: bool[source]: Determine if a Transformation is a one-to-many transformation. If a Transformation is a one-to-many transformation, the apply_transformation method should have a keyword arg “return_ranked_list” which allows for the transformed structures to be returned as a ranked list.

property use_multiprocessing[source]: Indicates whether the transformation can be applied by a subprocessing pool. This should be overridden to return True for transformations that the transmuter can parallelize.

class RemoveSitesTransformation(indices_to_remove)[source]

Bases: AbstractTransformation

Remove certain sites in a structure.

Parameters:: indices_to_remove – List of indices to remove. e.g. [0, 1, 2].

apply_transformation(structure: Structure)[source]

Apply the transformation.

Parameters:: structure (Structure) – A structurally similar structure in regards to crystal and site positions.
Returns:: A copy of structure with sites removed.

class ReplaceSiteSpeciesTransformation(indices_species_map)[source]

Bases: AbstractTransformation

This transformation substitutes certain sites with certain species.

Parameters:: indices_species_map – A dict containing the species mapping in int-string pairs. e.g. { 1:”Na”} or {2:”Mn2+”}. Multiple substitutions can be done. Overloaded to accept sp_and_occu dictionary. E.g. {1: {“Ge”:0.75, “C”:0.25} }, which substitutes a single species with multiple species to generate a disordered structure.

apply_transformation(structure: Structure)[source]

Apply the transformation.

Parameters:: structure (Structure) – A structurally similar structure in regards to crystal and site positions.
Returns:: A copy of structure with sites replaced.

class TranslateSitesTransformation(indices_to_move, translation_vector, vector_in_frac_coords=True)[source]

Bases: AbstractTransformation

This class translates a set of sites by a certain vector.

Parameters:

indices_to_move – The indices of the sites to move
translation_vector – Vector to move the sites. If a list of list or numpy array of shape, (len(indices_to_move), 3), is provided then each translation vector is applied to the corresponding site in the indices_to_move.
vector_in_frac_coords – Set to True if the translation vector is in fractional coordinates, and False if it is in cartesian coordinations. Defaults to True.

apply_transformation(structure: Structure)[source]

Apply the transformation.

Parameters:: structure (Structure) – A structurally similar structure in regards to crystal and site positions.
Returns:: A copy of structure with sites translated.

as_dict()[source]: JSON-serializable dict representation.

property inverse: TranslateSitesTransformation[source]: TranslateSitesTransformation with the reverse translation.

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.

class AutoOxiStateDecorationTransformation(symm_tol=0.1, max_radius=4, max_permutations=100000, distance_scale_factor=1.015, zeros_on_fail=False)[source]

Bases: AbstractTransformation

This 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.
zeros_on_fail (bool) – If True and the BVAnalyzer fails to come up with a guess for the oxidation states, we will set the all the oxidation states to zero.

apply_transformation(structure)[source]

Apply the transformation.

Parameters:: structure (Structure) – Input Structure
Returns:: Oxidation state decorated Structure.

class ChargedCellTransformation(charge=0)[source]

Bases: AbstractTransformation

The 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.

apply_transformation(structure)[source]

Apply the transformation.

Parameters:: structure (Structure) – Input Structure
Returns:: Charged Structure.

property inverse[source]

NotImplementedError.

Type:: Raises

class ConventionalCellTransformation(symprec: float = 0.01, angle_tolerance=5, international_monoclinic=True)[source]

Bases: AbstractTransformation

This 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.

apply_transformation(structure)[source]

Get most primitive cell for structure.

Parameters:: structure – A structure
Returns:: The same structure in a conventional standard setting

class DeformStructureTransformation(deformation=((1, 0, 0), (0, 1, 0), (0, 0, 1)))[source]

Bases: AbstractTransformation

This transformation deforms a structure by a deformation gradient matrix.

Parameters:: deformation (array) – deformation gradient for the transformation.

apply_transformation(structure)[source]

Apply the transformation.

Parameters:: structure (Structure) – Input Structure
Returns:: Deformed Structure.

property inverse[source]: Inverse Transformation.

class DiscretizeOccupanciesTransformation(max_denominator=5, tol: float | None = None, fix_denominator=False)[source]

Bases: AbstractTransformation

Discretize 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.

apply_transformation(structure) → Structure[source]

Discretize the site occupancies in the structure.

Parameters:: structure – disordered Structure to discretize occupancies
Returns:: new disordered Structure instance with occupancies discretized
Return type:: Structure

class OrderDisorderedStructureTransformation(algo: int = 0, symmetrized_structures: bool = False, no_oxi_states: bool = False, occ_tol: float = 0.25, symprec: float | None = None, angle_tolerance: float | None = None)[source]

Bases: AbstractTransformation

Order 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 order a large number of disordered sites can 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.
occ_tol (float) – Occupancy tolerance. If the total occupancy of a group is within this value of an integer, it will be rounded to that integer otherwise raise a ValueError. Defaults to 0.25.
symprec – float or None (default) If a float, and symmetrized_structures is True, the linear tolerance used to symmetrize structures with SpacegroupAnalyzer.
angle_tolerance – float or None (default) If a float, and symmetrized_structures is True, the angle tolerance used to symmetrize structures with SpacegroupAnalyzer.

ALGO_BEST_FIRST = 2[source]

ALGO_COMPLETE = 1[source]

ALGO_FAST = 0[source]

ALGO_RANDOM = -1[source]

apply_transformation(structure: Structure | SymmetrizedStructure, return_ranked_list: bool | int = False) → Structure | list[Structure] | list[dict[str, Any]][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 | int, optional) – If return_ranked_list is int, that number of structures is returned. If False, only the single lowest energy structure is returned. Defaults to False.

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.

property is_one_to_many: bool[source]: Transform one structure to many.

property lowest_energy_structure[source]: Lowest energy structure found.

class OxidationStateDecorationTransformation(oxidation_states)[source]

Bases: AbstractTransformation

This transformation decorates a structure with oxidation states.

Parameters:

oxidation_states (dict) – Oxidation states supplied as a dict,
{"Li" (e.g.) – 1, “O”:-2}.

apply_transformation(structure)[source]

Apply the transformation.

Parameters:: structure (Structure) – Input Structure
Returns:: Oxidation state decorated Structure.

class OxidationStateRemovalTransformation[source]

Bases: AbstractTransformation

This transformation removes oxidation states from a structure.

No arg needed.

apply_transformation(structure)[source]

Apply the transformation.

Parameters:: structure (Structure) – Input Structure
Returns:: Non-oxidation state decorated Structure.

class PartialRemoveSpecieTransformation(specie_to_remove, fraction_to_remove, algo=0)[source]

Bases: AbstractTransformation

Remove 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 – Species 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.

ALGO_BEST_FIRST = 2[source]

ALGO_COMPLETE = 1[source]

ALGO_ENUMERATE = 3[source]

ALGO_FAST = 0[source]

apply_transformation(structure: Structure, return_ranked_list: bool | int = False)[source]

Apply the transformation.

Parameters:

structure – input structure
return_ranked_list (bool | int, optional) –
If return_ranked_list is int, that number of structures

is returned. If False, only the single lowest energy structure is returned. Defaults to False.

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.

property is_one_to_many: bool[source]: Transform one structure to many.

class PerturbStructureTransformation(distance: float = 0.01, min_distance: float | None = None)[source]

Bases: AbstractTransformation

This transformation perturbs a structure by a specified distance in random directions. Used for breaking symmetries.

Parameters:

distance – Distance of perturbation in angstroms. All sites will be perturbed by exactly that distance in a random direction.
min_distance – Minimum distance for the perturbation range. Defaults to None, which means all
magnitude. (perturbations are the same)

apply_transformation(structure: Structure) → Structure[source]

Apply the transformation.

Parameters:: structure – Input Structure
Returns:: Structure with sites perturbed.

class PrimitiveCellTransformation(tolerance=0.5)[source]

Bases: AbstractTransformation

This 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.

apply_transformation(structure)[source]

Get most primitive cell for structure.

Parameters:: structure – A structure
Returns:: The most primitive structure found. The returned structure is guaranteed to have len(new structure) <= len(structure).

class RemoveSpeciesTransformation(species_to_remove)[source]

Bases: AbstractTransformation

Remove all occurrences of some species from a structure.

Parameters:: species_to_remove – List of species to remove. e.g. [“Li”, “Mn”].

apply_transformation(structure)[source]

Apply the transformation.

Parameters:: structure (Structure) – Input Structure
Returns:: Structure with species removed.

class RotationTransformation(axis, angle, angle_in_radians=False)[source]

Bases: AbstractTransformation

The 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.

apply_transformation(structure)[source]

Apply the transformation.

Parameters:: structure (Structure) – Input Structure
Returns:: Rotated Structure.

property inverse[source]: Inverse Transformation.

class ScaleToRelaxedTransformation(unrelaxed_structure, relaxed_structure, species_map=None)[source]

Bases: AbstractTransformation

Takes 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.

apply_transformation(structure)[source]

Get a copy of structure with lattice parameters and sites scaled to the same degree as the relaxed_structure.

Parameters:: structure (Structure) – A structurally similar structure in regards to crystal and site positions.

class SubstitutionTransformation(species_map: dict[SpeciesLike, SpeciesLike | dict[SpeciesLike, float]] | list[tuple[SpeciesLike, SpeciesLike]])[source]

Bases: AbstractTransformation

This 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.

apply_transformation(structure: Structure) → Structure[source]

Apply the transformation.

Parameters:: structure (Structure) – Input Structure
Returns:: Substituted Structure.

property inverse[source]: Inverse Transformation.

class SupercellTransformation(scaling_matrix=((1, 0, 0), (0, 1, 0), (0, 0, 1)))[source]

Bases: AbstractTransformation

The SupercellTransformation replicates a unit cell to a supercell.

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.

apply_transformation(structure)[source]

Apply the transformation.

Parameters:: structure (Structure) – Input Structure
Returns:: Supercell Structure.

classmethod from_boundary_distance(structure: Structure, min_boundary_dist: float = 6, allow_rotation: bool = False, max_atoms: float = -1) → Self[source]

Get a SupercellTransformation according to the desired minimum distance between periodic boundaries of the resulting supercell.

Parameters:

structure (Structure) – Input structure.
min_boundary_dist (float) – Desired minimum distance between all periodic boundaries. Defaults to 6.
allow_rotation (bool) – Whether allowing lattice angles to change. Only useful when at least two of the three lattice vectors are required to expand. Defaults to False. If True, a SupercellTransformation satisfying min_boundary_dist but with smaller number of atoms than the SupercellTransformation with unchanged lattice angles can possibly be found. If such a SupercellTransformation cannot be found easily, the SupercellTransformation with unchanged lattice angles will be returned.
max_atoms (int) – Maximum number of atoms allowed in the supercell. Defaults to -1 for infinity.

Returns:

SupercellTransformation.

classmethod from_scaling_factors(scale_a: float = 1, scale_b: float = 1, scale_c: float = 1) → Self[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.

property inverse[source]

NotImplementedError.

Type:: Raises

get_randomly_manipulated_structures(struct: Structure, manipulations: list, seed=None, n_return: int = 1) → list[Structure][source]

Get a structure with random manipulations applied.

Parameters:

struct – Input structure
manipulations – List of manipulations to apply
seed – Seed for random number generator
n_return – Number of structures to return

Returns:

List of structures with manipulations applied.

pymatgen.transformations.transformation_abc module

Abstract base class for structure transformations.

class AbstractTransformation[source]

Bases: MSONable, ABC

Abstract transformation class.

abstractmethod apply_transformation(structure: Structure) → Structure | list[dict[str, Any]] | list[Structure][source]

Apply the transformation to a structure. Depending on whether a transformation is one-to-many, there may be an option to return a ranked list of structures.

Parameters:

structure – input structure
return_ranked_list (bool | int, optional) –
If return_ranked_list is int, that number of structures

is returned. If False, only the single lowest energy structure is returned. Defaults to False.

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.

property inverse: AbstractTransformation | None[source]: The inverse transformation if available. Otherwise, should return None. Defaults to None, so only need to override if applicable.

property is_one_to_many: bool[source]: Determine if a Transformation is a one-to-many transformation. In that case, the apply_transformation method should have a keyword arg “return_ranked_list” which allows for the transformed structures to be returned as a ranked list. Defaults to False, so only need to override if True.

property use_multiprocessing: bool[source]: Indicates whether the transformation can be applied by a subprocessing pool. This should be overridden to return True for transformations that the transmuter can parallelize.