pymatgen.analysis.local_env module¶

class
JMolNN
(tol=0.001, el_radius_updates=None)[source]¶ Bases:
pymatgen.analysis.local_env.NearNeighbors
Determine nearneighbor 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 (default: 1E3).
 el_radius_updates – (dict) symbol>float to override default atomic radii table values

get_max_bond_distance
(el1_sym, el2_sym, constant=0.56)[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 :param constant: (float) factor to tune model
Returns: (float) max bond length

get_nn_info
(structure, n)[source]¶ Get all nearneighbor sites as well as the associated image locations and weights of the site with index n using the bond identification algorithm underlying JMol.
Parameters: Returns:  tuples, each one
of which represents a neighbor site, its image location, and its weight.
Return type: siw (list of tuples (Site, array, float))

class
MinimumDistanceNN
(tol=0.1, cutoff=10.0)[source]¶ Bases:
pymatgen.analysis.local_env.NearNeighbors
Determine nearneighbor sites and coordination number using the nearest neighbor(s) at distance, d_min, plus all neighbors within a distance (1 + delta) * d_min, where delta 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 nearneighbor sites (default: 10.0).

get_nn_info
(structure, n)[source]¶ Get all nearneighbor sites as well as the associated image locations and weights of the site with index n using the closest neighbor distancebased method.
Parameters: Returns:  tuples, each one
of which represents a neighbor site, its image location, and its weight.
Return type: siw (list of tuples (Site, array, float))

class
MinimumOKeeffeNN
(tol=0.1, cutoff=10.0)[source]¶ Bases:
pymatgen.analysis.local_env.NearNeighbors
Determine nearneighbor 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, 32263229) 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 nearneighbor sites (default: 10.0).

get_nn_info
(structure, n)[source]¶ Get all nearneighbor sites as well as the associated image locations and weights of the site with index n using the closest relative neighbor distancebased method with O’Keeffe parameters.
Parameters: Returns:  tuples, each one
of which represents a neighbor site, its image location, and its weight.
Return type: siw (list of tuples (Site, array, float))

class
MinimumVIRENN
(tol=0.1, cutoff=10.0)[source]¶ Bases:
pymatgen.analysis.local_env.NearNeighbors
Determine nearneighbor 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 nearneighbor sites (default: 10.0).

get_nn_info
(structure, n)[source]¶ Get all nearneighbor sites as well as the associated image locations and weights of the site with index n using the closest relative neighbor distancebased method with VIRE atomic/ionic radii.
Parameters: Returns:  tuples, each one
of which represents a neighbor site, its image location, and its weight.
Return type: siw (list of tuples (Site, array, float))

class
NearNeighbors
[source]¶ Bases:
object
Base class to determine near neighbors that typically include nearest neighbors and others that are within some tolerable distance.

get_cn
(structure, n, use_weights=False)[source]¶ Get coordination number, CN, of site with index n in structure.
Parameters: Returns: coordination number.
Return type: cn (integer or float)

get_nn
(structure, n)[source]¶ Get near neighbors of site with index n in structure.
Parameters: Returns: near neighbors.
Return type: sites (list of Site objects)

get_nn_images
(structure, n)[source]¶ Get image location of all near neighbors of site with index n in structure.
Parameters: Returns:  image locations of
near neighbors.
Return type: images (list of 3D integer array)

get_nn_info
(structure, n)[source]¶ Get all nearneighbor sites as well as the associated image locations and weights of the site with index n.
Parameters: 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 nearneighbor 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 of dicts)


class
ValenceIonicRadiusEvaluator
(structure)[source]¶ Bases:
object
Computes site valences and ionic radii for a structure using bond valence analyzer
Parameters: structure – pymatgen.core.structure.Structure 
radii
¶ List of ionic radii of elements in the order of sites.

structure
¶ Returns oxidation state decorated structure.

valences
¶ List of oxidation states of elements in the order of sites.


class
VoronoiNN
(tol=0, targets=None, cutoff=10.0, allow_pathological=False)[source]¶ Bases:
pymatgen.analysis.local_env.NearNeighbors
Uses a Voronoi algorithm to determine near neighbors for each site in a structure.
Parameters:  tol (float) – tolerance parameter for nearneighbor finding (default: 0).
 targets (Element or list of Elements) – target element(s).
 cutoff (float) – cutoff radius in Angstrom to look for nearneighbor atoms. Defaults to 10.0.
 allow_pathological (bool) – whether to allow infinite vertices in determination of Voronoi coordination.

get_nn_info
(structure, n)[source]¶ ” Get all nearneighbor sites as well as the associated image locations and weights of the site with index n in structure using Voronoi decomposition.
Parameters: Returns:  tuples, each one
of which represents a coordinated site, its image location, and its weight.
Return type: siw (list of tuples (Site, array, float))

get_voronoi_polyhedra
(structure, n)[source]¶ Gives a weighted polyhedra around a site. This uses the Voronoi construction with solid angle weights. See ref: A Proposed Rigorous Definition of Coordination Number, M. O’Keeffe, Acta Cryst. (1979). A35, 772775
Parameters: Returns: A dict of sites sharing a common Voronoi facet with the site n and their solid angle weights

get_neighbors_of_site_with_index
(struct, n, approach='min_dist', delta=0.1, cutoff=10.0)[source]¶ Returns the neighbors of a given site using a specific neighborfinding 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 neighborfinding 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 AllredRochow electronegativity. Values used are not generated from pymatgen, and are found in ‘okeeffe_params.json’.
Parameters: el2 (el1,) – two Element objects Returns: a float value of the predicted bond length

get_okeeffe_params
(el_symbol)[source]¶ Returns the elemental parameters related to atom size and electronegativity which are used for estimating bondvalence 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:  atomsize (‘r’) and electronegativityrelated (‘c’)
 parameter.
Return type: (dict)

site_is_of_motif_type
(struct, n, approach='min_dist', delta=0.1, cutoff=10.0, thresh=None)[source]¶ Returns the motif type of the site with index n in structure struct; currently featuring “tetrahedral”, “octahedral”, “bcc”, and “cp” (closepacked: 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 neighborfinding 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).