pymatgen.analysis.local_env module
This module provides classes to perform analyses of the local environments (e.g., finding near neighbors) of single sites in molecules and structures.
- class BrunnerNN_real(tol: float = 0.0001, cutoff=8.0)[source]
Bases:
NearNeighborsDetermine coordination number using Brunner’s algorithm which counts the atoms that are within the largest gap in differences in real space interatomic distances. This algorithm uses Brunner’s method of largest gap in interatomic distances.
- Parameters:
tol (float) – tolerance parameter for bond determination (default: 1E-4).
cutoff (float) – cutoff radius in Angstrom to look for near-neighbor atoms. Defaults to 8.0.
- get_nn_info(structure: Structure, n: int)[source]
Get all near-neighbor sites as well as the associated image locations and weights of the site with index n in structure.
- class BrunnerNN_reciprocal(tol: float = 0.0001, cutoff=8.0)[source]
Bases:
NearNeighborsDetermine coordination number using Brunner’s algorithm which counts the atoms that are within the largest gap in differences in real space interatomic distances. This algorithm uses Brunner’s method of largest reciprocal gap in interatomic distances.
- Parameters:
tol (float) – tolerance parameter for bond determination (default: 1E-4).
cutoff (float) – cutoff radius in Angstrom to look for near-neighbor atoms. Defaults to 8.0.
- get_nn_info(structure: Structure, n: int)[source]
Get all near-neighbor sites as well as the associated image locations and weights of the site with index n in structure.
- class BrunnerNN_relative(tol: float = 0.0001, cutoff=8.0)[source]
Bases:
NearNeighborsDetermine coordination number using Brunner’s algorithm which counts the atoms that are within the largest gap in differences in real space interatomic distances. This algorithm uses Brunner’s method of of largest relative gap in interatomic distances.
- Parameters:
tol (float) – tolerance parameter for bond determination (default: 1E-4).
cutoff (float) – cutoff radius in Angstrom to look for near-neighbor atoms. Defaults to 8.0.
- get_nn_info(structure: Structure, n: int)[source]
Get all near-neighbor sites as well as the associated image locations and weights of the site with index n in structure.
- class CovalentBondNN(tol: float = 0.2, order=True)[source]
Bases:
NearNeighborsDetermine near-neighbor sites and bond orders using built-in pymatgen.Molecule CovalentBond functionality.
NOTE: This strategy is only appropriate for molecules, and not for structures.
- Parameters:
tol (float) – Tolerance for covalent bond checking.
order (bool) – If True (default), this class will compute bond orders. If False, bond lengths will be computed
- property extend_structure_molecules[source]
Do Molecules need to be converted to Structures to use this NearNeighbors class? Note: this property is not defined for classes for which molecules_allowed is False.
- Type:
Boolean property
- get_bonded_structure(structure: Structure, decorate: bool = False) MoleculeGraph[source]
Obtain a MoleculeGraph object using this NearNeighbor class.
- Parameters:
structure – Molecule object.
decorate (bool) – whether to annotate site properties
by (with order parameters using neighbors determined) –
class (this NearNeighbor) –
Returns: a pymatgen.analysis.graphs.MoleculeGraph object
- get_nn_info(structure: Structure, n: int)[source]
Get all near-neighbor sites and weights (orders) of bonds for a given atom.
- Parameters:
structure – input Molecule.
n – index of site for which to determine near neighbors.
- Returns:
[dict] representing a neighboring site and the type of bond present between site n and the neighboring site.
- get_nn_shell_info(structure: Structure, site_idx, shell)[source]
Get a certain nearest neighbor shell for a certain site.
Determines all non-backtracking paths through the neighbor network computed by get_nn_info. The weight is determined by multiplying the weight of the neighbor at each hop through the network. For example, a 2nd-nearest-neighbor that has a weight of 1 from its 1st-nearest-neighbor and weight 0.5 from the original site will be assigned a weight of 0.5.
As this calculation may involve computing the nearest neighbors of atoms multiple times, the calculation starts by computing all of the neighbor info and then calling _get_nn_shell_info. If you are likely to call this method for more than one site, consider calling get_all_nn first and then calling this protected method yourself.
- Parameters:
structure (Molecule) – Input structure
site_idx (int) – index of site for which to determine neighbor information.
shell (int) – Which neighbor shell to retrieve (1 == 1st NN shell)
- Returns:
- list of dictionaries. Each entry in the list is information about
a certain neighbor in the structure, in the same format as get_nn_info.
- class Critic2NN[source]
Bases:
NearNeighborsPerforms a topological analysis using critic2 to obtain neighbor information, using a sum of atomic charge densities. If an actual charge density is available (e.g. from a VASP CHGCAR), see Critic2Caller directly instead.
Init for Critic2NN.
- property extend_structure_molecules[source]
Do Molecules need to be converted to Structures to use this NearNeighbors class? Note: this property is not defined for classes for which molecules_allowed is False.
- Type:
Boolean property
- get_bonded_structure(structure: Structure, decorate: bool = False) StructureGraph[source]
- Parameters:
structure (Structure) – Input structure
decorate (bool, optional) – Whether to decorate the structure. Defaults to False.
- Returns:
Bonded structure
- Return type:
- get_nn_info(structure: Structure, n: int)[source]
Get all near-neighbor sites as well as the associated image locations and weights of the site with index n in structure.
- class CrystalNN(weighted_cn=False, cation_anion=False, distance_cutoffs=(0.5, 1), x_diff_weight=3.0, porous_adjustment=True, search_cutoff=7, fingerprint_length=None)[source]
Bases:
NearNeighborsThis is a custom near-neighbor method intended for use in all kinds of periodic structures (metals, minerals, porous structures, etc). It is based on a Voronoi algorithm and uses the solid angle weights to determine the probability of various coordination environments. The algorithm can also modify probability using smooth distance cutoffs as well as Pauling electronegativity differences. The output can either be the most probable coordination environment or a weighted list of coordination environments.
Initialize CrystalNN with desired parameters. Default parameters assume “chemical bond” type behavior is desired. For geometric neighbor finding (e.g., structural framework), set (i) distance_cutoffs=None, (ii) x_diff_weight=0 and (optionally) (iii) porous_adjustment=False which will disregard the atomic identities and perform best for a purely geometric match.
- Parameters:
weighted_cn – (bool) if set to True, will return fractional weights for each potential near neighbor.
cation_anion – (bool) if set True, will restrict bonding targets to sites with opposite or zero charge. Requires an oxidation states on all sites in the structure.
distance_cutoffs – ([float, float]) - if not None, penalizes neighbor distances greater than sum of covalent radii plus distance_cutoffs[0]. Distances greater than covalent radii sum plus distance_cutoffs[1] are enforced to have zero weight.
x_diff_weight – (float) - if multiple types of neighbor elements are possible, this sets preferences for targets with higher electronegativity difference.
porous_adjustment – (bool) - if True, readjusts Voronoi weights to better describe layered / porous structures
search_cutoff – (float) cutoff in Angstroms for initial neighbor search; this will be adjusted if needed internally
fingerprint_length – (int) if a fixed_length CN “fingerprint” is desired from get_nn_data(), set this parameter
- class NNData(all_nninfo, cn_weights, cn_nninfo)[source]
Bases:
tupleCreate new instance of NNData(all_nninfo, cn_weights, cn_nninfo)
- get_cn(structure: Structure, n: int, **kwargs) float[source]
Get coordination number, CN, of site with index n in structure.
- Parameters:
structure (Structure) – input structure.
n (int) – index of site for which to determine CN.
use_weights (bool) – flag indicating whether (True) to use weights for computing the coordination number or not (False, default: each coordinated site has equal weight).
on_disorder ('take_majority_strict' | 'take_majority_drop' | 'take_max_species' | 'error') – What to do when encountering a disordered structure. ‘error’ will raise ValueError. ‘take_majority_strict’ will use the majority specie on each site and raise ValueError if no majority exists. ‘take_max_species’ will use the first max specie on each site. For {{Fe: 0.4, O: 0.4, C: 0.2}}, ‘error’ and ‘take_majority_strict’ will raise ValueError, while ‘take_majority_drop’ ignores this site altogether and ‘take_max_species’ will use Fe as the site specie.
- Returns:
coordination number.
- Return type:
cn (int or float)
- get_cn_dict(structure: Structure, n: int, use_weights: bool = False, **kwargs)[source]
Get coordination number, CN, of each element bonded to site with index n in structure
- Parameters:
structure (Structure) – input structure
n (int) – index of site for which to determine CN.
use_weights (bool) – flag indicating whether (True) to use weights for computing the coordination number or not (False, default: each coordinated site has equal weight).
- Returns:
dictionary of CN of each element bonded to site
- Return type:
cn (dict)
- get_nn_data(structure: Structure, n: int, length=None)[source]
The main logic of the method to compute near neighbor.
- Parameters:
structure – (Structure) enclosing structure object
n – (int) index of target site to get NN info for
length – (int) if set, will return a fixed range of CN numbers
- Returns:
all near neighbor sites with weights
a dict of CN -> weight
a dict of CN -> associated near neighbor sites
- Return type:
a namedtuple (NNData) object that contains
- get_nn_info(structure: Structure, n: int) list[dict][source]
Get all near-neighbor information.
- Parameters:
structure – (Structure) pymatgen Structure
n – (int) index of target site
- Returns:
- each dictionary provides information
about a single near neighbor, where key ‘site’ gives access to the corresponding Site object, ‘image’ gives the image location, and ‘weight’ provides the weight that a given near-neighbor site contributes to the coordination number (1 or smaller), ‘site_index’ gives index of the corresponding site in the original structure.
- Return type:
siw (list[dict])
- property molecules_allowed[source]
can this NearNeighbors class be used with Molecule objects?
- Type:
Boolean property
- class CutOffDictNN(cut_off_dict=None)[source]
Bases:
NearNeighborsA basic NN class using a dictionary of fixed cut-off distances. Only pairs of elements listed in the cut-off dictionary are considered during construction of the neighbor lists.
Omit passing a dictionary for a Null/Empty NN class.
- Parameters:
cut_off_dict (dict[str, float]) – a dictionary
distances (of cut-off) – 2.0} for
{ (e.g.) – 2.0} for
Angstroms. (a maximum Fe-O bond length of 2) –
listed (Bonds will only be created between pairs) –
dictionary. (in the cut-off) –
decorated (If your structure is oxidation state) –
:param : :param the cut-off distances will have to explicitly include: :param the oxidation state: 2.0} :type the oxidation state: ‘Fe2+’, ‘O2-’ :param e.g. {: 2.0} :type e.g. {: ‘Fe2+’, ‘O2-’
- property extend_structure_molecules[source]
Do Molecules need to be converted to Structures to use this NearNeighbors class? Note: this property is not defined for classes for which molecules_allowed is False.
- Type:
Boolean property
- static from_preset(preset)[source]
Initialise a CutOffDictNN according to a preset set of cut-offs.
- Parameters:
preset (str) –
A preset name. The list of supported presets are:
”vesta_2019”: The distance cut-offs used by the VESTA visualisation program.
- Returns:
A CutOffDictNN using the preset cut-off dictionary.
- get_nn_info(structure: Structure, n: int)[source]
Get all near-neighbor sites as well as the associated image locations and weights of the site with index n in structure.
- class EconNN(tol: float = 0.2, cutoff: float = 10.0, cation_anion: bool = False, use_fictive_radius: bool = False)[source]
Bases:
NearNeighborsDetermines the average effective coordination number for each cation in a given structure using Hoppe’s algorithm.
This method follows the procedure outlined in:
Hoppe, Rudolf. “Effective coordination numbers (ECoN) and mean fictive ionic radii (MEFIR).” Zeitschrift für Kristallographie-Crystalline Materials 150.1-4 (1979): 23-52.
- Parameters:
tol – Tolerance parameter for bond determination.
cutoff – Cutoff radius in Angstrom to look for near-neighbor atoms.
cation_anion – If set to True, will restrict bonding targets to sites with opposite or zero charge. Requires an oxidation states on all sites in the structure.
use_fictive_radius – Whether to use the fictive radius in the EcoN calculation. If False, the bond distance will be used.
- property extend_structure_molecules[source]
Do Molecules need to be converted to Structures to use this NearNeighbors class? Note: this property is not defined for classes for which molecules_allowed is False.
- Type:
Boolean property
- get_nn_info(structure: Structure, n: int)[source]
Get all near-neighbor sites as well as the associated image locations and weights of the site with index n in structure.
- class IsayevNN(tol: float = 0.25, targets: Element | list[Element] | None = None, cutoff: float = 13.0, allow_pathological: bool = False, extra_nn_info: bool = True, compute_adj_neighbors: bool = True)[source]
Bases:
VoronoiNNUses the algorithm defined in 10.1038/ncomms15679
Sites are considered neighbors if (i) they share a Voronoi facet and (ii) the bond distance is less than the sum of the Cordero covalent radii + 0.25 Å.
- Parameters:
tol – Tolerance in Å for bond distances that are considered coordinated.
targets – Target element(s).
cutoff – Cutoff radius in Angstrom to look for near-neighbor atoms.
allow_pathological – Whether to allow infinite vertices in Voronoi coordination.
extra_nn_info – Add all polyhedron info to get_nn_info.
compute_adj_neighbors – Whether to compute which neighbors are adjacent. Turn off for faster performance.
- get_all_nn_info(structure: Structure) list[list[dict[str, Any]]][source]
- Parameters:
structure (Structure) – input structure.
- Returns:
List of near neighbor information for each site. See get_nn_info for the format of the data for each site.
- get_nn_info(structure: Structure, n: int) list[dict[str, Any]][source]
Get all near-neighbor site information.
Gets the associated image locations and weights of the site with index n in structure using Voronoi decomposition and distance cutoff.
- Parameters:
structure – Input structure.
n – Index of site for which to determine near-neighbor sites.
- Returns:
List of dicts containing the near-neighbor information. Each dict has the keys:
”site”: The near-neighbor site.
”image”: The periodic image of the near-neighbor site.
”weight”: The face weight of the Voronoi decomposition.
”site_index”: The index of the near-neighbor site in the original structure.
- class JmolNN(tol: float = 0.45, min_bond_distance: float = 0.4, el_radius_updates: dict[SpeciesLike, float] | None = None)[source]
Bases:
NearNeighborsDetermine near-neighbor sites and coordination number using an emulation of Jmol’s default autoBond() algorithm. This version of the algorithm does not take into account any information regarding known charge states.
- Parameters:
tol (float) – tolerance parameter for bond determination Defaults to 0.56.
min_bond_distance (float) – minimum bond distance for consideration Defaults to 0.4.
el_radius_updates – (dict) symbol->float to override default atomic radii table values
- property extend_structure_molecules[source]
Do Molecules need to be converted to Structures to use this NearNeighbors class? Note: this property is not defined for classes for which molecules_allowed is False.
- Type:
Boolean property
- get_max_bond_distance(el1_sym, el2_sym)[source]
Use Jmol algorithm to determine bond length from atomic parameters :param el1_sym: (str) symbol of atom 1 :param el2_sym: (str) symbol of atom 2
Returns: (float) max bond length
- get_nn_info(structure: Structure, n: int)[source]
Get all near-neighbor sites as well as the associated image locations and weights of the site with index n using the bond identification algorithm underlying Jmol.
- class LocalStructOrderParams(types, parameters=None, cutoff=-10.0)[source]
Bases:
objectThis class permits the calculation of various types of local structure order parameters.
- Parameters:
types ([string]) –
list of strings representing the types of order parameters to be calculated. Note that multiple mentions of the same type may occur. Currently available types recognize following environments:
- ”cn”: simple coordination number—normalized
if desired;
”sgl_bd”: single bonds; “bent”: bent (angular) coordinations
(Zimmermann & Jain, in progress, 2017);
”T”: T-shape coordinations; “see_saw_rect”: see saw-like coordinations; “tet”: tetrahedra
(Zimmermann et al., submitted, 2017);
- ”oct”: octahedra
(Zimmermann et al., submitted, 2017);
- ”bcc”: body-centered cubic environments (Peters,
Chem. Phys., 131, 244103, 2009);
”tri_plan”: trigonal planar environments; “sq_plan”: square planar environments; “pent_plan”: pentagonal planar environments; “tri_pyr”: trigonal pyramids (coordinated atom is in
the center of the basal plane);
”sq_pyr”: square pyramids; “pent_pyr”: pentagonal pyramids; “hex_pyr”: hexagonal pyramids; “tri_bipyr”: trigonal bipyramids; “sq_bipyr”: square bipyramids; “pent_bipyr”: pentagonal bipyramids; “hex_bipyr”: hexagonal bipyramids; “cuboct”: cuboctahedra; “q2”: motif-unspecific bond orientational order
parameter (BOOP) of weight l=2 (Steinhardt et al., Phys. Rev. B, 28, 784-805, 1983);
”q4”: BOOP of weight l=4; “q6”: BOOP of weight l=6. “reg_tri”: regular triangle with varying height
to basal plane;
”sq”: square coordination (cf., “reg_tri”); “oct_legacy”: original Peters-style OP recognizing
octahedral coordination environments (Zimmermann et al., J. Am. Chem. Soc., 137, 13352-13361, 2015) that can, however, produce small negative values sometimes.
”sq_pyr_legacy”: square pyramids (legacy);
parameters ([dict]) –
list of dictionaries that store float-type parameters associated with the definitions of the different order parameters (length of list = number of OPs). If an entry is None, default values are used that are read from the op_params.yaml file. With few exceptions, 9 different parameters are used across all OPs:
- ”norm”: normalizing constant (used in “cn”
(default value: 1)).
- ”TA”: target angle (TA) in fraction of 180 degrees
(“bent” (1), “tet” (0.6081734479693927), “tri_plan” (0.66666666667), “pent_plan” (0.6), “sq_pyr_legacy” (0.5)).
- ”IGW_TA”: inverse Gaussian width (IGW) for penalizing
angles away from the target angle in inverse fractions of 180 degrees to (“bent” and “tet” (15), “tri_plan” (13.5), “pent_plan” (18), “sq_pyr_legacy” (30)).
- ”IGW_EP”: IGW for penalizing angles away from the
equatorial plane (EP) at 90 degrees (“T”, “see_saw_rect”, “oct”, “sq_plan”, “tri_pyr”, “sq_pyr”, “pent_pyr”, “hex_pyr”, “tri_bipyr”, “sq_bipyr”, “pent_bipyr”, “hex_bipyr”, and “oct_legacy” (18)).
- ”fac_AA”: factor applied to azimuth angle (AA) in cosine
term (“T”, “tri_plan”, and “sq_plan” (1), “tet”, “tri_pyr”, and “tri_bipyr” (1.5), “oct”, “sq_pyr”, “sq_bipyr”, and “oct_legacy” (2), “pent_pyr” and “pent_bipyr” (2.5), “hex_pyr” and “hex_bipyr” (3)).
- ”exp_cos_AA”: exponent applied to cosine term of AA
(“T”, “tet”, “oct”, “tri_plan”, “sq_plan”, “tri_pyr”, “sq_pyr”, “pent_pyr”, “hex_pyr”, “tri_bipyr”, “sq_bipyr”, “pent_bipyr”, “hex_bipyr”, and “oct_legacy” (2)).
- ”min_SPP”: smallest angle (in radians) to consider
a neighbor to be at South pole position (“see_saw_rect”, “oct”, “bcc”, “sq_plan”, “tri_bipyr”, “sq_bipyr”, “pent_bipyr”, “hex_bipyr”, “cuboct”, and “oct_legacy” (2.792526803190927)).
- ”IGW_SPP”: IGW for penalizing angles away from South
pole position (“see_saw_rect”, “oct”, “bcc”, “sq_plan”, “tri_bipyr”, “sq_bipyr”, “pent_bipyr”, “hex_bipyr”, “cuboct”, and “oct_legacy” (15)).
- ”w_SPP”: weight for South pole position relative to
equatorial positions (“see_saw_rect” and “sq_plan” (1), “cuboct” (1.8), “tri_bipyr” (2), “oct”, “sq_bipyr”, and “oct_legacy” (3), “pent_bipyr” (4), “hex_bipyr” (5), “bcc” (6)).
cutoff (float) – Cutoff radius to determine which nearest neighbors are supposed to contribute to the order parameters. If the value is negative the neighboring sites found by distance and cutoff radius are further pruned using the get_nn method from the VoronoiNN class.
- compute_trigonometric_terms(thetas, phis)[source]
Computes trigonometric terms that are required to calculate bond orientational order parameters using internal variables.
- Parameters:
thetas ([float]) – polar angles of all neighbors in radians.
phis ([float]) – azimuth angles of all neighbors in radians. The list of azimuth angles of all neighbors in radians. The list of azimuth angles is expected to have the same size as the list of polar angles; otherwise, a ValueError is raised. Also, the two lists of angles have to be coherent in order. That is, it is expected that the order in the list of azimuth angles corresponds to a distinct sequence of neighbors. And, this sequence has to equal the sequence of neighbors in the list of polar angles.
- get_order_parameters(structure: Structure, n: int, indices_neighs: list[int] | None = None, tol: float = 0.0, target_spec=None)[source]
Compute all order parameters of site n.
- Parameters:
structure (Structure) – input structure.
n (int) – index of site in input structure, for which OPs are to be calculated. Note that we do not use the sites iterator here, but directly access sites via struct[index].
indices_neighs (list[int]) – list of indices of those neighbors in Structure object structure that are to be considered for OP computation. This optional argument overwrites the way neighbors are to be determined as defined in the constructor (i.e., Voronoi coordination finder via negative cutoff radius vs constant cutoff radius if cutoff was positive). We do not use information about the underlying structure lattice if the neighbor indices are explicitly provided. This has two important consequences. First, the input Structure object can, in fact, be a simple list of Site objects. Second, no nearest images of neighbors are determined when providing an index list. Note furthermore that this neighbor determination type ignores the optional target_spec argument.
tol (float) – threshold of weight (= solid angle / maximal solid angle) to determine if a particular pair is considered neighbors; this is relevant only in the case when Voronoi polyhedra are used to determine coordination
target_spec (Species) – target species to be considered when calculating the order parameters of site n; None includes all species of input structure.
- Returns:
representing order parameters. Should it not be possible to compute a given OP for a conceptual reason, the corresponding entry is None instead of a float. For Steinhardt et al.’s bond orientational OPs and the other geometric OPs (“tet”, “oct”, “bcc”, etc.), this can happen if there is a single neighbor around site n in the structure because that does not permit calculation of angles between multiple neighbors.
- Return type:
[floats]
- get_parameters(index)[source]
Returns list of floats that represents the parameters associated with calculation of the order parameter that was defined at the index provided. Attention: the parameters do not need to equal those originally inputted because of processing out of efficiency reasons.
- Parameters:
index (int) – index of order parameter for which associated parameters are to be returned.
- Returns:
parameters of a given OP.
- Return type:
[float]
- get_q2(thetas=None, phis=None)[source]
Calculates the value of the bond orientational order parameter of weight l=2. If the function is called with non-empty lists of polar and azimuthal angles the corresponding trigonometric terms are computed afresh. Otherwise, it is expected that the compute_trigonometric_terms function has been just called.
- Parameters:
thetas ([float]) – polar angles of all neighbors in radians.
phis ([float]) – azimuth angles of all neighbors in radians.
- Returns:
- bond orientational order parameter of weight l=2
corresponding to the input angles thetas and phis.
- Return type:
float
- get_q4(thetas=None, phis=None)[source]
Calculates the value of the bond orientational order parameter of weight l=4. If the function is called with non-empty lists of polar and azimuthal angles the corresponding trigonometric terms are computed afresh. Otherwise, it is expected that the compute_trigonometric_terms function has been just called.
- Parameters:
thetas ([float]) – polar angles of all neighbors in radians.
phis ([float]) – azimuth angles of all neighbors in radians.
- Returns:
- bond orientational order parameter of weight l=4
corresponding to the input angles thetas and phis.
- Return type:
float
- get_q6(thetas=None, phis=None)[source]
Calculates the value of the bond orientational order parameter of weight l=6. If the function is called with non-empty lists of polar and azimuthal angles the corresponding trigonometric terms are computed afresh. Otherwise, it is expected that the compute_trigonometric_terms function has been just called.
- Parameters:
thetas ([float]) – polar angles of all neighbors in radians.
phis ([float]) – azimuth angles of all neighbors in radians.
- Returns:
- bond orientational order parameter of weight l=6
corresponding to the input angles thetas and phis.
- Return type:
float
- get_type(index)[source]
Return type of order parameter at the index provided and represented by a short string.
- Parameters:
index (int) – index of order parameter for which type is to be returned.
- Returns:
OP type.
- Return type:
str
- class MinimumDistanceNN(tol: float = 0.1, cutoff=10, get_all_sites=False)[source]
Bases:
NearNeighborsDetermine near-neighbor sites and coordination number using the nearest neighbor(s) at distance, d_min, plus all neighbors within a distance (1 + tol) * d_min, where tol is a (relative) distance tolerance parameter.
- Parameters:
tol (float) – tolerance parameter for neighbor identification (default: 0.1).
cutoff (float) – cutoff radius in Angstrom to look for trial near-neighbor sites (default: 10).
get_all_sites (bool) – If this is set to True then the neighbor sites are only determined by the cutoff radius, tol is ignored
- property extend_structure_molecules[source]
Do Molecules need to be converted to Structures to use this NearNeighbors class? Note: this property is not defined for classes for which molecules_allowed is False.
- Type:
Boolean property
- get_nn_info(structure: Structure, n: int) list[dict[str, Any]][source]
Get all near-neighbor sites as well as the associated image locations and weights of the site with index n using the closest neighbor distance-based method.
- Parameters:
structure (Structure) – input structure.
n (int) – index of site for which to determine near neighbors.
- Returns:
- dicts with (Site, array, float) each one of which represents a
neighbor site, its image location, and its weight.
- Return type:
siw (list[dict])
- class MinimumOKeeffeNN(tol: float = 0.1, cutoff=10)[source]
Bases:
NearNeighborsDetermine near-neighbor sites and coordination number using the neighbor(s) at closest relative distance, d_min_OKeffee, plus some relative tolerance, where bond valence parameters from O’Keeffe’s bond valence method (J. Am. Chem. Soc. 1991, 3226-3229) are used to calculate relative distances.
- Parameters:
tol (float) – tolerance parameter for neighbor identification (default: 0.1).
cutoff (float) – cutoff radius in Angstrom to look for trial near-neighbor sites (default: 10).
- property extend_structure_molecules[source]
Do Molecules need to be converted to Structures to use this NearNeighbors class? Note: this property is not defined for classes for which molecules_allowed is False.
- Type:
Boolean property
- get_nn_info(structure: Structure, n: int)[source]
Get all near-neighbor sites as well as the associated image locations and weights of the site with index n using the closest relative neighbor distance-based method with O’Keeffe parameters.
- class MinimumVIRENN(tol: float = 0.1, cutoff=10)[source]
Bases:
NearNeighborsDetermine near-neighbor sites and coordination number using the neighbor(s) at closest relative distance, d_min_VIRE, plus some relative tolerance, where atom radii from the ValenceIonicRadiusEvaluator (VIRE) are used to calculate relative distances.
- Parameters:
tol (float) – tolerance parameter for neighbor identification (default: 0.1).
cutoff (float) – cutoff radius in Angstrom to look for trial near-neighbor sites (default: 10).
- get_nn_info(structure: Structure, n: int)[source]
Get all near-neighbor sites as well as the associated image locations and weights of the site with index n using the closest relative neighbor distance-based method with VIRE atomic/ionic radii.
- class NearNeighbors[source]
Bases:
objectBase class to determine near neighbors that typically include nearest neighbors and others that are within some tolerable distance.
- property extend_structure_molecules: bool[source]
Do Molecules need to be converted to Structures to use this NearNeighbors class? Note: this property is not defined for classes for which molecules_allowed is False.
- Type:
Boolean property
- get_all_nn_info(structure)[source]
Get a listing of all neighbors for all sites in a structure
- Parameters:
structure (Structure) – Input structure
- Returns:
- List of NN site information for each site in the structure. Each
entry has the same format as get_nn_info
- get_bonded_structure(structure: Structure, decorate: bool = False, weights: bool = True, edge_properties: bool = False, on_disorder: on_disorder_options = 'take_majority_strict') StructureGraph | MoleculeGraph[source]
Obtain a StructureGraph object using this NearNeighbor class. Requires the optional dependency networkx (pip install networkx).
- Parameters:
structure – Structure object.
decorate (bool) – whether to annotate site properties with order parameters using neighbors determined by this NearNeighbor class
weights (bool) – whether to include edge weights from NearNeighbor class in StructureGraph
edge_properties (bool) –
on_disorder ('take_majority_strict' | 'take_majority_drop' | 'take_max_species' | 'error') – What to do when encountering a disordered structure. ‘error’ will raise ValueError. ‘take_majority_strict’ will use the majority specie on each site and raise ValueError if no majority exists. ‘take_max_species’ will use the first max specie on each site. For {{Fe: 0.4, O: 0.4, C: 0.2}}, ‘error’ and ‘take_majority_strict’ will raise ValueError, while ‘take_majority_drop’ ignores this site altogether and ‘take_max_species’ will use Fe as the site specie.
Returns: a pymatgen.analysis.graphs.StructureGraph object
- get_cn(structure: Structure, n: int, use_weights: bool = False, on_disorder: Literal['take_majority_strict', 'take_majority_drop', 'take_max_species', 'error'] = 'take_majority_strict') float[source]
Get coordination number, CN, of site with index n in structure.
- Parameters:
structure (Structure) – input structure.
n (int) – index of site for which to determine CN.
use_weights (bool) – flag indicating whether (True) to use weights for computing the coordination number or not (False, default: each coordinated site has equal weight).
on_disorder ('take_majority_strict' | 'take_majority_drop' | 'take_max_species' | 'error') – What to do when encountering a disordered structure. ‘error’ will raise ValueError. ‘take_majority_strict’ will use the majority specie on each site and raise ValueError if no majority exists. ‘take_max_species’ will use the first max specie on each site. For {{Fe: 0.4, O: 0.4, C: 0.2}}, ‘error’ and ‘take_majority_strict’ will raise ValueError, while ‘take_majority_drop’ ignores this site altogether and ‘take_max_species’ will use Fe as the site specie.
- Returns:
coordination number.
- Return type:
cn (int or float)
- get_cn_dict(structure: Structure, n: int, use_weights: bool = False)[source]
Get coordination number, CN, of each element bonded to site with index n in structure
- Parameters:
structure (Structure) – input structure
n (int) – index of site for which to determine CN.
use_weights (bool) – flag indicating whether (True) to use weights for computing the coordination number or not (False, default: each coordinated site has equal weight).
- Returns:
dictionary of CN of each element bonded to site
- Return type:
cn (dict)
- get_local_order_parameters(structure: Structure, n: int)[source]
Calculate those local structure order parameters for the given site whose ideal CN corresponds to the underlying motif (e.g., CN=4, then calculate the square planar, tetrahedral, see-saw-like, rectangular see-saw-like order parameters).
- Parameters:
structure – Structure object
n (int) – site index.
- Returns (dict[str, float]):
A dict of order parameters (values) and the underlying motif type (keys; for example, tetrahedral).
- get_nn(structure: Structure, n: int)[source]
Get near neighbors of site with index n in structure.
- Parameters:
structure (Structure) – input structure.
n (int) – index of site in structure for which to determine neighbors.
- Returns:
near neighbors.
- Return type:
sites (list of Site objects)
- get_nn_images(structure: Structure, n: int)[source]
Get image location of all near neighbors of site with index n in structure.
- Parameters:
structure (Structure) – input structure.
n (int) – index of site for which to determine the image location of near neighbors.
- Returns:
- image locations of
near neighbors.
- Return type:
images (list of 3D integer array)
- get_nn_info(structure: Structure, n: int) list[dict][source]
Get all near-neighbor sites as well as the associated image locations and weights of the site with index n.
- Parameters:
structure (Structure) – input structure.
n (int) – index of site for which to determine near-neighbor information.
- Returns:
- each dictionary provides information
about a single near neighbor, where key ‘site’ gives access to the corresponding Site object, ‘image’ gives the image location, and ‘weight’ provides the weight that a given near-neighbor site contributes to the coordination number (1 or smaller), ‘site_index’ gives index of the corresponding site in the original structure.
- Return type:
siw (list[dict])
- get_nn_shell_info(structure: Structure, site_idx, shell)[source]
Get a certain nearest neighbor shell for a certain site.
Determines all non-backtracking paths through the neighbor network computed by get_nn_info. The weight is determined by multiplying the weight of the neighbor at each hop through the network. For example, a 2nd-nearest-neighbor that has a weight of 1 from its 1st-nearest-neighbor and weight 0.5 from the original site will be assigned a weight of 0.5.
As this calculation may involve computing the nearest neighbors of atoms multiple times, the calculation starts by computing all of the neighbor info and then calling _get_nn_shell_info. If you are likely to call this method for more than one site, consider calling get_all_nn first and then calling this protected method yourself.
- Parameters:
structure (Structure) – Input structure
site_idx (int) – index of site for which to determine neighbor information.
shell (int) – Which neighbor shell to retrieve (1 == 1st NN shell)
- Returns:
- list of dictionaries. Each entry in the list is information about
a certain neighbor in the structure, in the same format as get_nn_info.
- get_weights_of_nn_sites(structure: Structure, n: int)[source]
Get weight associated with each near neighbor of site with index n in structure.
- Parameters:
structure (Structure) – input structure.
n (int) – index of site for which to determine the weights.
- Returns:
near-neighbor weights.
- Return type:
weights (list of floats)
- class OpenBabelNN(order=True)[source]
Bases:
NearNeighborsDetermine near-neighbor sites and bond orders using OpenBabel API.
NOTE: This strategy is only appropriate for molecules, and not for structures.
- Parameters:
order (bool) – True if bond order should be returned as a weight, False
weight. (if bond length should be used as a) –
- property extend_structure_molecules[source]
Do Molecules need to be converted to Structures to use this NearNeighbors class? Note: this property is not defined for classes for which molecules_allowed is False.
- Type:
Boolean property
- get_bonded_structure(structure: Structure, decorate: bool = False) StructureGraph[source]
Obtain a MoleculeGraph object using this NearNeighbor class. Requires the optional dependency networkx (pip install networkx).
- Parameters:
structure – Molecule object.
decorate (bool) – whether to annotate site properties
by (with order parameters using neighbors determined) –
class (this NearNeighbor) –
Returns: a pymatgen.analysis.graphs.MoleculeGraph object
- get_nn_info(structure: Structure, n: int)[source]
Get all near-neighbor sites and weights (orders) of bonds for a given atom.
- Parameters:
structure – Molecule object.
n – index of site for which to determine near neighbors.
- Returns:
representing a neighboring site and the type of bond present between site n and the neighboring site.
- Return type:
(dict)
- get_nn_shell_info(structure: Structure, site_idx, shell)[source]
Get a certain nearest neighbor shell for a certain site.
Determines all non-backtracking paths through the neighbor network computed by get_nn_info. The weight is determined by multiplying the weight of the neighbor at each hop through the network. For example, a 2nd-nearest-neighbor that has a weight of 1 from its 1st-nearest-neighbor and weight 0.5 from the original site will be assigned a weight of 0.5.
As this calculation may involve computing the nearest neighbors of atoms multiple times, the calculation starts by computing all of the neighbor info and then calling _get_nn_shell_info. If you are likely to call this method for more than one site, consider calling get_all_nn first and then calling this protected method yourself.
- Parameters:
structure (Molecule) – Input structure
site_idx (int) – index of site for which to determine neighbor information.
shell (int) – Which neighbor shell to retrieve (1 == 1st NN shell)
- Returns:
- list of dictionaries. Each entry in the list is information about
a certain neighbor in the structure, in the same format as get_nn_info.
- class ValenceIonicRadiusEvaluator(structure)[source]
Bases:
objectComputes site valences and ionic radii for a structure using bond valence analyzer
- Parameters:
structure – pymatgen.core.structure.Structure
- class VoronoiNN(tol=0, targets=None, cutoff=13.0, allow_pathological=False, weight='solid_angle', extra_nn_info=True, compute_adj_neighbors=True)[source]
Bases:
NearNeighborsUses a Voronoi algorithm to determine near neighbors for each site in a structure.
- Parameters:
tol (float) – tolerance parameter for near-neighbor finding. Faces that are smaller than tol fraction of the largest face are not included in the tessellation. (default: 0).
targets (Element or list of Elements) – target element(s).
cutoff (float) – cutoff radius in Angstrom to look for near-neighbor atoms. Defaults to 13.0.
allow_pathological (bool) – whether to allow infinite vertices in determination of Voronoi coordination.
weight (string) – available in get_voronoi_polyhedra)
extra_nn_info (bool) –
compute_adj_neighbors (bool) – adjacent. Turn off for faster performance
- get_all_nn_info(structure)[source]
- Parameters:
structure (Structure) – input structure.
- Returns:
All nn info for all sites.
- get_all_voronoi_polyhedra(structure)[source]
Get the Voronoi polyhedra for all site in a simulation cell
- Parameters:
structure (Structure) – Structure to be evaluated
- Returns:
A dict of sites sharing a common Voronoi facet with the site n mapped to a directory containing statistics about the facet:
solid_angle - Solid angle subtended by face
- angle_normalized - Solid angle normalized such that the
faces with the largest
area - Area of the facet
face_dist - Distance between site n and the facet
volume - Volume of Voronoi cell for this face
n_verts - Number of vertices on the facet
- get_nn_info(structure: Structure, n: int)[source]
Get all near-neighbor sites as well as the associated image locations and weights of the site with index n in structure using Voronoi decomposition.
- get_voronoi_polyhedra(structure: Structure, n: int)[source]
Gives a weighted polyhedra around a site.
See ref: A Proposed Rigorous Definition of Coordination Number, M. O’Keeffe, Acta Cryst. (1979). A35, 772-775
- Parameters:
structure (Structure) – structure for which to evaluate the coordination environment.
n (int) – site index.
- Returns:
A dict of sites sharing a common Voronoi facet with the site n mapped to a directory containing statistics about the facet:
solid_angle - Solid angle subtended by face
- angle_normalized - Solid angle normalized such that the
faces with the largest
area - Area of the facet
face_dist - Distance between site n and the facet
volume - Volume of Voronoi cell for this face
n_verts - Number of vertices on the facet
- get_neighbors_of_site_with_index(struct, n, approach='min_dist', delta=0.1, cutoff=10)[source]
Returns the neighbors of a given site using a specific neighbor-finding method.
- Parameters:
struct (Structure) – input structure.
n (int) – index of site in Structure object for which motif type is to be determined.
approach (str) – type of neighbor-finding approach, where “min_dist” will use the MinimumDistanceNN class, “voronoi” the VoronoiNN class, “min_OKeeffe” the MinimumOKeeffe class, and “min_VIRE” the MinimumVIRENN class.
delta (float) – tolerance involved in neighbor finding.
cutoff (float) – (large) radius to find tentative neighbors.
Returns: neighbor sites.
- get_okeeffe_distance_prediction(el1, el2)[source]
Returns an estimate of the bond valence parameter (bond length) using the derived parameters from ‘Atoms Sizes and Bond Lengths in Molecules and Crystals’ (O’Keeffe & Brese, 1991). The estimate is based on two experimental parameters: r and c. The value for r is based off radius, while c is (usually) the Allred-Rochow electronegativity. Values used are not generated from pymatgen, and are found in ‘okeeffe_params.json’.
- get_okeeffe_params(el_symbol)[source]
Returns the elemental parameters related to atom size and electronegativity which are used for estimating bond-valence parameters (bond length) of pairs of atoms on the basis of data provided in ‘Atoms Sizes and Bond Lengths in Molecules and Crystals’ (O’Keeffe & Brese, 1991).
- Parameters:
el_symbol (str) – element symbol.
- Returns:
- atom-size (‘r’) and electronegativity-related (‘c’)
parameter.
- Return type:
(dict)
- gramschmidt(vin, uin)[source]
Returns that part of the first input vector that is orthogonal to the second input vector. The output vector is not normalized.
- Parameters:
vin (numpy array) – first input vector
uin (numpy array) – second input vector
- metal_edge_extender(mol_graph, cutoff: float = 2.5, metals: list | tuple | None = ('Li', 'Mg', 'Ca', 'Zn', 'B', 'Al'), coordinators: list | tuple = ('O', 'N', 'F', 'S', 'Cl'))[source]
Function to identify and add missed coordinate bond edges for metals
- Parameters:
mol_graph – pymatgen.analysis.graphs.MoleculeGraph object
cutoff – cutoff in Angstrom. Metal-coordinator sites that are closer together than this value will be considered coordination bonds. If the MoleculeGraph contains a metal, but no coordination bonds are found with the chosen cutoff, the cutoff will be increased by 1 Angstrom and another attempt will be made to identify coordination bonds.
metals – Species considered metals for the purpose of identifying missed coordinate bond edges. The set {“Li”, “Mg”, “Ca”, “Zn”, “B”, “Al”} (default) corresponds to the settings used in the LIBE dataset. Alternatively, set to None to cause any Species classified as a metal by Specie.is_metal to be considered a metal.
coordinators – Possible coordinating species to consider when identifying missed coordinate bonds. The default set {“O”, “N”, “F”, “S”, “Cl”} was used in the LIBE dataset.
- Returns:
- pymatgen.analysis.graphs.MoleculeGraph object with additional
metal bonds (if any found) added
- Return type:
mol_graph
- oxygen_edge_extender(mol_graph: MoleculeGraph) MoleculeGraph[source]
Identify and add missed O-C or O-H bonds. This is particularly important when oxygen is forming three bonds, e.g. in H3O+ or XOH2+. See https://github.com/materialsproject/pymatgen/pull/2903 for details.
- Parameters:
mol_graph (MoleculeGraph) – molecule graph to extend
- Returns:
object with additional O-C or O-H bonds added (if any found)
- Return type:
- site_is_of_motif_type(struct, n, approach='min_dist', delta=0.1, cutoff=10, thresh=None)[source]
Returns the motif type of the site with index n in structure struct; currently featuring “tetrahedral”, “octahedral”, “bcc”, and “cp” (close-packed: fcc and hcp) as well as “square pyramidal” and “trigonal bipyramidal”. If the site is not recognized, “unrecognized” is returned. If a site should be assigned to two different motifs, “multiple assignments” is returned.
- Parameters:
struct (Structure) – input structure.
n (int) – index of site in Structure object for which motif type is to be determined.
approach (str) – type of neighbor-finding approach, where “min_dist” will use the MinimumDistanceNN class, “voronoi” the VoronoiNN class, “min_OKeeffe” the MinimumOKeeffe class, and “min_VIRE” the MinimumVIRENN class.
delta (float) – tolerance involved in neighbor finding.
cutoff (float) – (large) radius to find tentative neighbors.
thresh (dict) – thresholds for motif criteria (currently, required keys and their default values are “qtet”: 0.5, “qoct”: 0.5, “qbcc”: 0.5, “q6”: 0.4).
Returns: motif type (str).
- solid_angle(center, coords)[source]
Helper method to calculate the solid angle of a set of coords from the center.
- Parameters:
center (3x1 array) – Center to measure solid angle from.
coords (Nx3 array) – List of coords to determine solid angle.
- Returns:
The solid angle.
- vol_tetra(vt1, vt2, vt3, vt4)[source]
Calculate the volume of a tetrahedron, given the four vertices of vt1, vt2, vt3 and vt4.
- Parameters:
vt1 (array-like) – coordinates of vertex 1.
vt2 (array-like) – coordinates of vertex 2.
vt3 (array-like) – coordinates of vertex 3.
vt4 (array-like) – coordinates of vertex 4.
- Returns:
volume of the tetrahedron.
- Return type:
(float)