Source code for pymatgen.analysis.structure_analyzer

# coding: utf-8
# Copyright (c) Pymatgen Development Team.
# Distributed under the terms of the MIT License.

This module provides classes to perform topological analyses of structures.

__author__ = "Shyue Ping Ong, Geoffroy Hautier, Sai Jayaraman"
__copyright__ = "Copyright 2011, The Materials Project"

from math import pi, acos
import numpy as np
import itertools
import collections

from import deprecated

from warnings import warn
from scipy.spatial import Voronoi
from pymatgen import PeriodicSite
from pymatgen import Element, Specie, Composition
from pymatgen.util.num import abs_cap
from pymatgen.symmetry.analyzer import SpacegroupAnalyzer
from pymatgen.core.surface import SlabGenerator
from pymatgen.analysis.local_env import VoronoiNN, JmolNN

[docs]def average_coordination_number(structures, freq=10): """ Calculates the ensemble averaged Voronoi coordination numbers of a list of Structures using VoronoiNN. Typically used for analyzing the output of a Molecular Dynamics run. Args: structures (list): list of Structures. freq (int): sampling frequency of coordination number [every freq steps]. Returns: Dictionary of elements as keys and average coordination numbers as values. """ coordination_numbers = {} for spec in structures[0].composition.as_dict().keys(): coordination_numbers[spec] = 0.0 count = 0 for t in range(len(structures)): if t % freq != 0: continue count += 1 vnn = VoronoiNN() for atom in range(len(structures[0])): cn = vnn.get_cn(structures[t], atom, use_weights=True) coordination_numbers[structures[t][atom].species_string] += cn elements = structures[0].composition.as_dict() for el in coordination_numbers: coordination_numbers[el] = coordination_numbers[el] / elements[ el] / count return coordination_numbers
[docs]class VoronoiAnalyzer: """ Performs a statistical analysis of Voronoi polyhedra around each site. Each Voronoi polyhedron is described using Schaefli notation. That is a set of indices {c_i} where c_i is the number of faces with i number of vertices. E.g. for a bcc crystal, there is only one polyhedron notation of which is [0,6,0,8,0,0,...]. In perfect crystals, these also corresponds to the Wigner-Seitz cells. For distorted-crystals, liquids or amorphous structures, rather than one-type, there is a statistical distribution of polyhedra. See ref: Microstructure and its relaxation in Fe-B amorphous system simulated by molecular dynamics, Stepanyuk et al., J. Non-cryst. Solids (1993), 159, 80-87. """ def __init__(self, cutoff=5.0, qhull_options="Qbb Qc Qz"): """ Args: cutoff (float): cutoff distance to search for neighbors of a given atom (default = 5.0) qhull_options (str): options to pass to qhull (optional) """ self.cutoff = cutoff self.qhull_options = qhull_options
[docs] def analyze(self, structure, n=0): """ Performs Voronoi analysis and returns the polyhedra around atom n in Schlaefli notation. Args: structure (Structure): structure to analyze n (int): index of the center atom in structure Returns: voronoi index of n: <c3,c4,c6,c6,c7,c8,c9,c10> where c_i denotes number of facets with i vertices. """ center = structure[n] neighbors = structure.get_sites_in_sphere(center.coords, self.cutoff) neighbors = [i[0] for i in sorted(neighbors, key=lambda s: s[1])] qvoronoi_input = np.array([s.coords for s in neighbors]) voro = Voronoi(qvoronoi_input, qhull_options=self.qhull_options) vor_index = np.array([0, 0, 0, 0, 0, 0, 0, 0]) for key in voro.ridge_dict: if 0 in key: # This means if the center atom is in key if -1 in key: # This means if an infinity point is in key raise ValueError("Cutoff too short.") else: try: vor_index[len(voro.ridge_dict[key]) - 3] += 1 except IndexError: # If a facet has more than 10 edges, it's skipped here. pass return vor_index
[docs] def analyze_structures(self, structures, step_freq=10, most_frequent_polyhedra=15): """ Perform Voronoi analysis on a list of Structures. Note that this might take a significant amount of time depending on the size and number of structures. Args: structures (list): list of Structures cutoff (float: cutoff distance around an atom to search for neighbors step_freq (int): perform analysis every step_freq steps qhull_options (str): options to pass to qhull most_frequent_polyhedra (int): this many unique polyhedra with highest frequences is stored. Returns: A list of tuples in the form (voronoi_index,frequency) """ voro_dict = {} step = 0 for structure in structures: step += 1 if step % step_freq != 0: continue v = [] for n in range(len(structure)): v.append(str(self.analyze(structure, n=n).view())) for voro in v: if voro in voro_dict: voro_dict[voro] += 1 else: voro_dict[voro] = 1 return sorted(voro_dict.items(), key=lambda x: (x[1], x[0]), reverse=True)[:most_frequent_polyhedra]
[docs] @staticmethod def plot_vor_analysis(voronoi_ensemble): """ Plot the Voronoi analysis. :param voronoi_ensemble: :return: matplotlib.pyplot """ t = zip(*voronoi_ensemble) labels = t[0] val = list(t[1]) tot = np.sum(val) val = [float(j) / tot for j in val] pos = np.arange(len(val)) + .5 # the bar centers on the y axis import matplotlib.pyplot as plt plt.figure() plt.barh(pos, val, align='center', alpha=0.5) plt.yticks(pos, labels) plt.xlabel('Count') plt.title('Voronoi Spectra') plt.grid(True) return plt
[docs]class RelaxationAnalyzer: """ This class analyzes the relaxation in a calculation. """ def __init__(self, initial_structure, final_structure): """ Please note that the input and final structures should have the same ordering of sites. This is typically the case for most computational codes. Args: initial_structure (Structure): Initial input structure to calculation. final_structure (Structure): Final output structure from calculation. """ if final_structure.formula != initial_structure.formula: raise ValueError("Initial and final structures have different " + "formulas!") self.initial = initial_structure = final_structure
[docs] def get_percentage_volume_change(self): """ Returns the percentage volume change. Returns: Volume change in percentage, e.g., 0.055 implies a 5.5% increase. """ initial_vol = self.initial.lattice.volume final_vol = return final_vol / initial_vol - 1
[docs] def get_percentage_lattice_parameter_changes(self): """ Returns the percentage lattice parameter changes. Returns: A dict of the percentage change in lattice parameter, e.g., {'a': 0.012, 'b': 0.021, 'c': -0.031} implies a change of 1.2%, 2.1% and -3.1% in the a, b and c lattice parameters respectively. """ initial_latt = self.initial.lattice final_latt = d = {l: getattr(final_latt, l) / getattr(initial_latt, l) - 1 for l in ["a", "b", "c"]} return d
[docs] def get_percentage_bond_dist_changes(self, max_radius=3.0): """ Returns the percentage bond distance changes for each site up to a maximum radius for nearest neighbors. Args: max_radius (float): Maximum radius to search for nearest neighbors. This radius is applied to the initial structure, not the final structure. Returns: Bond distance changes as a dict of dicts. E.g., {index1: {index2: 0.011, ...}}. For economy of representation, the index1 is always less than index2, i.e., since bonding between site1 and siten is the same as bonding between siten and site1, there is no reason to duplicate the information or computation. """ data = collections.defaultdict(dict) for inds in itertools.combinations(list(range(len(self.initial))), 2): (i, j) = sorted(inds) initial_dist = self.initial[i].distance(self.initial[j]) if initial_dist < max_radius: final_dist =[i].distance([j]) data[i][j] = final_dist / initial_dist - 1 return data
[docs]class VoronoiConnectivity: """ Computes the solid angles swept out by the shared face of the voronoi polyhedron between two sites. """ def __init__(self, structure, cutoff=10): """ Args: structure (Structure): Input structure cutoff (float) Cutoff distance. """ self.cutoff = cutoff self.s = structure recp_len = np.array( i = np.ceil(cutoff * recp_len / (2 * pi)) offsets = np.mgrid[-i[0]:i[0] + 1, -i[1]:i[1] + 1, -i[2]:i[2] + 1].T self.offsets = np.reshape(offsets, (-1, 3)) # shape = [image, axis] self.cart_offsets = self.s.lattice.get_cartesian_coords(self.offsets) @property def connectivity_array(self): """ Provides connectivity array. Returns: connectivity: An array of shape [atomi, atomj, imagej]. atomi is the index of the atom in the input structure. Since the second atom can be outside of the unit cell, it must be described by both an atom index and an image index. Array data is the solid angle of polygon between atomi and imagej of atomj """ # shape = [site, axis] cart_coords = np.array(self.s.cart_coords) # shape = [site, image, axis] all_sites = cart_coords[:, None, :] + self.cart_offsets[None, :, :] vt = Voronoi(all_sites.reshape((-1, 3))) n_images = all_sites.shape[1] cs = (len(self.s), len(self.s), len(self.cart_offsets)) connectivity = np.zeros(cs) vts = np.array(vt.vertices) for (ki, kj), v in vt.ridge_dict.items(): atomi = ki // n_images atomj = kj // n_images imagei = ki % n_images imagej = kj % n_images if imagei != n_images // 2 and imagej != n_images // 2: continue if imagei == n_images // 2: # atomi is in original cell val = solid_angle(vt.points[ki], vts[v]) connectivity[atomi, atomj, imagej] = val if imagej == n_images // 2: # atomj is in original cell val = solid_angle(vt.points[kj], vts[v]) connectivity[atomj, atomi, imagei] = val if -10.101 in vts[v]: warn('Found connectivity with infinite vertex. ' 'Cutoff is too low, and results may be ' 'incorrect') return connectivity @property def max_connectivity(self): """ returns the 2d array [sitei, sitej] that represents the maximum connectivity of site i to any periodic image of site j """ return np.max(self.connectivity_array, axis=2)
[docs] def get_connections(self): """ Returns a list of site pairs that are Voronoi Neighbors, along with their real-space distances. """ con = [] maxconn = self.max_connectivity for ii in range(0, maxconn.shape[0]): for jj in range(0, maxconn.shape[1]): if maxconn[ii][jj] != 0: dist = self.s.get_distance(ii, jj) con.append([ii, jj, dist]) return con
[docs] def get_sitej(self, site_index, image_index): """ Assuming there is some value in the connectivity array at indices (1, 3, 12). sitei can be obtained directly from the input structure (structure[1]). sitej can be obtained by passing 3, 12 to this function Args: site_index (int): index of the site (3 in the example) image_index (int): index of the image (12 in the example) """ atoms_n_occu = self.s[site_index].species lattice = self.s.lattice coords = self.s[site_index].frac_coords + self.offsets[image_index] return PeriodicSite(atoms_n_occu, coords, lattice)
[docs]def solid_angle(center, coords): """ Helper method to calculate the solid angle of a set of coords from the center. Args: center (3x1 array): Center to measure solid angle from. coords (Nx3 array): List of coords to determine solid angle. Returns: The solid angle. """ o = np.array(center) r = [np.array(c) - o for c in coords] r.append(r[0]) n = [np.cross(r[i + 1], r[i]) for i in range(len(r) - 1)] n.append(np.cross(r[1], r[0])) vals = [] for i in range(len(n) - 1): v =[i], n[i + 1]) \ / (np.linalg.norm(n[i]) * np.linalg.norm(n[i + 1])) vals.append(acos(abs_cap(v))) phi = sum(vals) return phi + (3 - len(r)) * pi
[docs]def get_max_bond_lengths(structure, el_radius_updates=None): """ Provides max bond length estimates for a structure based on the JMol table and algorithms. Args: structure: (structure) el_radius_updates: (dict) symbol->float to update atomic radii Returns: (dict) - (Element1, Element2) -> float. The two elements are ordered by Z. """ # jmc = JMolCoordFinder(el_radius_updates) jmnn = JmolNN(el_radius_updates=el_radius_updates) bonds_lens = {} els = sorted(structure.composition.elements, key=lambda x: x.Z) for i1 in range(len(els)): for i2 in range(len(els) - i1): bonds_lens[els[i1], els[i1 + i2]] = jmnn.get_max_bond_distance( els[i1].symbol, els[i1 + i2].symbol) return bonds_lens
@deprecated(message=("find_dimension has been moved to" "pymatgen.analysis.dimensionality.get_dimensionality_gorai" " this method will be removed in pymatgen v2019.1.1.")) def get_dimensionality(structure, max_hkl=2, el_radius_updates=None, min_slab_size=5, min_vacuum_size=5, standardize=True, bonds=None): """ This method returns whether a structure is 3D, 2D (layered), or 1D (linear chains or molecules) according to the algorithm published in Gorai, P., Toberer, E. & Stevanovic, V. Computational Identification of Promising Thermoelectric Materials Among Known Quasi-2D Binary Compounds. J. Mater. Chem. A 2, 4136 (2016). Note that a 1D structure detection might indicate problems in the bonding algorithm, particularly for ionic crystals (e.g., NaCl) Users can change the behavior of bonds detection by passing either el_radius_updates to update atomic radii for auto-detection of max bond distances, or bonds to explicitly specify max bond distances for atom pairs. Note that if you pass both, el_radius_updates are ignored. Args: structure: (Structure) structure to analyze dimensionality for max_hkl: (int) max index of planes to look for layers el_radius_updates: (dict) symbol->float to update atomic radii min_slab_size: (float) internal surface construction parameter min_vacuum_size: (float) internal surface construction parameter standardize (bool): whether to standardize the structure before analysis. Set to False only if you already have the structure in a convention where layers / chains will be along low <hkl> indexes. bonds ({(specie1, specie2): max_bond_dist}: bonds are specified as a dict of tuples: float of specie1, specie2 and the max bonding distance. For example, PO4 groups may be defined as {("P", "O"): 3}. Returns: (int) the dimensionality of the structure - 1 (molecules/chains), 2 (layered), or 3 (3D) """ if standardize: structure = SpacegroupAnalyzer(structure). \ get_conventional_standard_structure() if not bonds: bonds = get_max_bond_lengths(structure, el_radius_updates) num_surfaces = 0 for h in range(max_hkl): for k in range(max_hkl): for l in range(max_hkl): if max([h, k, l]) > 0 and num_surfaces < 2: sg = SlabGenerator(structure, (h, k, l), min_slab_size=min_slab_size, min_vacuum_size=min_vacuum_size) slabs = sg.get_slabs(bonds) for _ in slabs: num_surfaces += 1 return 3 - min(num_surfaces, 2)
[docs]def contains_peroxide(structure, relative_cutoff=1.1): """ Determines if a structure contains peroxide anions. Args: structure (Structure): Input structure. relative_cutoff: The peroxide bond distance is 1.49 Angstrom. Relative_cutoff * 1.49 stipulates the maximum distance two O atoms must be to each other to be considered a peroxide. Returns: Boolean indicating if structure contains a peroxide anion. """ ox_type = oxide_type(structure, relative_cutoff) if ox_type == "peroxide": return True else: return False
[docs]class OxideType: """ Separate class for determining oxide type. """ def __init__(self, structure, relative_cutoff=1.1): """ Args: structure: Input structure. relative_cutoff: Relative_cutoff * act. cutoff stipulates the max. distance two O atoms must be from each other. Default value is 1.1. At most 1.1 is recommended, nothing larger, otherwise the script cannot distinguish between superoxides and peroxides. """ self.structure = structure self.relative_cutoff = relative_cutoff self.oxide_type, self.nbonds = self.parse_oxide()
[docs] def parse_oxide(self): """ Determines if an oxide is a peroxide/superoxide/ozonide/normal oxide. Returns: oxide_type (str): Type of oxide ozonide/peroxide/superoxide/hydroxide/None. nbonds (int): Number of peroxide/superoxide/hydroxide bonds in structure. """ structure = self.structure relative_cutoff = self.relative_cutoff o_sites_frac_coords = [] h_sites_frac_coords = [] lattice = structure.lattice if isinstance(structure.composition.elements[0], Element): comp = structure.composition elif isinstance(structure.composition.elements[0], Specie): elmap = collections.defaultdict(float) for site in structure: for species, occu in site.species.items(): elmap[species.element] += occu comp = Composition(elmap) if Element("O") not in comp or comp.is_element: return "None", 0 for site in structure: syms = [sp.symbol for sp in site.species.keys()] if "O" in syms: o_sites_frac_coords.append(site.frac_coords) if "H" in syms: h_sites_frac_coords.append(site.frac_coords) if h_sites_frac_coords: dist_matrix = lattice.get_all_distances(o_sites_frac_coords, h_sites_frac_coords) if np.any(dist_matrix < relative_cutoff * 0.93): return "hydroxide", len( np.where(dist_matrix < relative_cutoff * 0.93)[0]) / 2.0 dist_matrix = lattice.get_all_distances(o_sites_frac_coords, o_sites_frac_coords) np.fill_diagonal(dist_matrix, 1000) is_superoxide = False is_peroxide = False is_ozonide = False if np.any(dist_matrix < relative_cutoff * 1.35): bond_atoms = np.where(dist_matrix < relative_cutoff * 1.35)[0] is_superoxide = True elif np.any(dist_matrix < relative_cutoff * 1.49): is_peroxide = True bond_atoms = np.where(dist_matrix < relative_cutoff * 1.49)[0] if is_superoxide: if len(bond_atoms) > len(set(bond_atoms)): is_superoxide = False is_ozonide = True try: nbonds = len(set(bond_atoms)) except UnboundLocalError: nbonds = 0.0 if is_ozonide: str_oxide = "ozonide" elif is_superoxide: str_oxide = "superoxide" elif is_peroxide: str_oxide = "peroxide" else: str_oxide = "oxide" if str_oxide == "oxide": nbonds = comp["O"] return str_oxide, nbonds
[docs]def oxide_type(structure, relative_cutoff=1.1, return_nbonds=False): """ Determines if an oxide is a peroxide/superoxide/ozonide/normal oxide Args: structure (Structure): Input structure. relative_cutoff (float): Relative_cutoff * act. cutoff stipulates the max distance two O atoms must be from each other. return_nbonds (bool): Should number of bonds be requested? """ ox_obj = OxideType(structure, relative_cutoff) if return_nbonds: return ox_obj.oxide_type, ox_obj.nbonds else: return ox_obj.oxide_type
[docs]def sulfide_type(structure): """ Determines if a structure is a sulfide/polysulfide Args: structure (Structure): Input structure. Returns: (str) sulfide/polysulfide/sulfate """ structure = structure.copy() structure.remove_oxidation_states() s = Element("S") comp = structure.composition if comp.is_element or s not in comp: return None finder = SpacegroupAnalyzer(structure, symprec=0.1) symm_structure = finder.get_symmetrized_structure() s_sites = [sites[0] for sites in symm_structure.equivalent_sites if sites[0].specie == s] def process_site(site): # in an exceptionally rare number of structures, the search # radius needs to be increased to find a neighbor atom search_radius = 4 neighbors = [] while len(neighbors) == 0: neighbors = structure.get_neighbors(site, search_radius) search_radius *= 2 if search_radius > max( * 2: break neighbors = sorted(neighbors, key=lambda n: n.nn_distance) dist = neighbors[0].nn_distance coord_elements = [nn.specie for nn in neighbors if nn.nn_distance < dist + 0.4][:4] avg_electroneg = np.mean([e.X for e in coord_elements]) if avg_electroneg > s.X: return "sulfate" elif avg_electroneg == s.X and s in coord_elements: return "polysulfide" else: return "sulfide" types = set([process_site(site) for site in s_sites]) if "sulfate" in types: return None elif "polysulfide" in types: return "polysulfide" else: return "sulfide"