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:
pymatgen.transformations.transformation_abc.AbstractTransformation
Create absorbate structures.
Use AdsorbateSiteFinder to add an absorbate to a slab.
 Parameters
adsorbate (Molecule) – molecule to add as adsorbate
selective_dynamics (bool) – flag for whether to assign nonsurface 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 (3tuple 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}

class
ChargeBalanceTransformation
(charge_balance_sp)[source]¶ Bases:
pymatgen.transformations.transformation_abc.AbstractTransformation
This is a transformation that disorders a structure to make it charge balanced, given an oxidation statedecorated structure.
 Parameters
charge_balance_sp – specie to add or remove. Currently only removal is supported

class
CubicSupercellTransformation
(min_atoms: Optional[int] = None, max_atoms: Optional[int] = None, min_length: float = 15.0, force_diagonal: bool = False)[source]¶ Bases:
pymatgen.transformations.transformation_abc.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 nonsingular). 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.
force_diagonal – If True, return a transformation with a diagonal transformation matrix.

apply_transformation
(structure: pymatgen.core.structure.Structure) → pymatgen.core.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 nonsingular). 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

class
DisorderOrderedTransformation
(max_sites_to_merge=2)[source]¶ Bases:
pymatgen.transformations.transformation_abc.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

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:
pymatgen.transformations.transformation_abc.AbstractTransformation
A transformation that performs doping of a structure.
 Parameters
dopant (Specieslike) – 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.

class
EnumerateStructureTransformation
(min_cell_size=1, max_cell_size=1, symm_prec=0.1, refine_structure=False, enum_precision_parameter=0.001, check_ordered_symmetry=True, max_disordered_sites=None, sort_criteria='ewald', timeout=None)[source]¶ Bases:
pymatgen.transformations.transformation_abc.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, refinment 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) – Sort by Ewald energy (“ewald”, must have oxidation states and slow) or by number of sites (“nsites”, much faster).
timeout (float) – timeout in minutes to pass to EnumlibAdaptor

apply_transformation
(structure, return_ranked_list=False)[source]¶ Returns either a single ordered structure or a sequence of all ordered structures.
 Parameters
structure – 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 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.

class
GrainBoundaryTransformation
(rotation_axis, rotation_angle, expand_times=4, vacuum_thickness=0.0, ab_shift=None, normal=False, ratio=True, plane=None, max_search=20, tol_coi=1e08, rm_ratio=0.7, quick_gen=False)[source]¶ Bases:
pymatgen.transformations.transformation_abc.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 (list of float, in unit of a, b vectors of Gb) – in plane shift of two grains
normal (logic) – determine if need to require the c axis of top grain (first transformation matrix) perperdicular 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.
 Returns
Grain boundary structure (gb (Structure) object).

class
MagOrderParameterConstraint
(order_parameter, species_constraints=None, site_constraint_name=None, site_constraints=None)[source]¶ Bases:
monty.json.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
(float) (order_parameter) – any number from 0.0 to 1.0, typically 0.5 (antiferromagnetic) or 1.0 (ferromagnetic)
(list) (site_constraints) – str or list of strings of Species symbols that the constraint should apply to
(str) (site_constraint_name) – name of the site property that the constraint should apply to, e.g. “coordination_no”
(list) – list of values of the site property that the constraints should apply to

class
MagOrderingTransformation
(mag_species_spin, order_parameter=0.5, energy_model=<pymatgen.analysis.energy_models.SymmetryModel object>, **kwargs)[source]¶ Bases:
pymatgen.transformations.transformation_abc.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}
(float or list) (order_parameter) – 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 :class: 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, return_ranked_list=False)[source]¶ Apply MagOrderTransformation to an input structure. :param structure: Any ordered structure. :param return_ranked_list: As in other Transformations. :return:

class
MonteCarloRattleTransformation
(**kwargs)[source]¶ Bases:
pymatgen.transformations.transformation_abc.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: pymatgen.core.structure.Structure) → pymatgen.core.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
Performs 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.
Performs 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.

class
SQSTransformation
(scaling, cluster_size_and_shell=None, search_time=60, directory=None, instances=None, temperature=1, wr=1, wn=1, wd=0.5, tol=0.001, best_only=True, remove_duplicate_structures=True, reduction_algo='LLL')[source]¶ Bases:
pymatgen.transformations.transformation_abc.AbstractTransformation
A transformation that creates a special quasirandom structure (SQS) from a structure with partial occupancies.
 Parameters
structure (Structure) – Disordered pymatgen Structure object
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
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
 Keyword Arguments
search_time (float) – Time spent looking for the ideal SQS in minutes (default: 60)
directory (str) – Directory to run mcsqs calculation and store files (default: None runs calculations in a temp directory)
instances (int) – Specifies the number of parallel instances of mcsqs to run (default: number of cpu cores detected by Python)
temperature (int or float) – Monte Carlo temperature (default: 1), “T” in atat code
wr (int or float) – Weight assigned to range of perfect correlation match in objective function (default = 1)
wn (int or float) – Multiplicative decrease in weight per additional point in cluster (default: 1)
wd (int or float) – Exponent of decay in weight as function of cluster diameter (default: 0)
tol (int or float) – Tolerance for matching correlations (default: 1e3)
best_only (bool) – only return structures with lowest objective function
remove_duplicate_structures (bool) – only return unique structures
reduction_algo (str) – The lattice reduction algorithm to use. Currently supported options are “niggli” or “LLL”. “False” does not reduce structure.

apply_transformation
(structure, return_ranked_list=False)[source]¶ Applies SQS transformation :param structure: pymatgen Structure with partial occupancies :type structure: pymatgen Structure :param return_ranked_list: number of structures to return :type return_ranked_list: bool
 Returns
pymatgen Structure which is an SQS of the input structure

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:
pymatgen.transformations.transformation_abc.AbstractTransformation
A transformation that creates a slab from a structure.
 Parameters
miller_index (3tuple 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
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

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:
pymatgen.transformations.transformation_abc.AbstractTransformation
Use AdsorptionSiteFinder to perform substitutiontype 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 nonsurface 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, return_ranked_list=False)[source]¶  Parameters
structure – Must be a Slab structure
return_ranked_list – Whether or not multiple structures are returned. If return_ranked_list is a number, up to that number of structures is returned.
Returns: Slab with sites substituted

class
SubstitutionPredictorTransformation
(threshold=0.01, scale_volumes=True, **kwargs)[source]¶ Bases:
pymatgen.transformations.transformation_abc.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

class
SuperTransformation
(transformations, nstructures_per_trans=1)[source]¶ Bases:
pymatgen.transformations.transformation_abc.AbstractTransformation
This is a transformation that is inherently onetomany. 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 onetomany and, nstructures_per_trans structures from each transformation are added to the full list. Defaults to 1, i.e., only best structure.