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site_transformations.py
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site_transformations.py
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# Copyright (c) Pymatgen Development Team.
# Distributed under the terms of the MIT License.
"""
This module defines site transformations which transforms a structure into
another structure. Site transformations differ from standard transformations
in that they operate in a site-specific manner.
All transformations should inherit the AbstractTransformation ABC.
"""
from __future__ import annotations
import itertools
import logging
import math
import time
import numpy as np
from monty.json import MSONable
from pymatgen.analysis.ewald import EwaldMinimizer, EwaldSummation
from pymatgen.analysis.local_env import MinimumDistanceNN
from pymatgen.core.sites import PeriodicSite
from pymatgen.core.structure import Structure
from pymatgen.symmetry.analyzer import SpacegroupAnalyzer
from pymatgen.transformations.transformation_abc import AbstractTransformation
class InsertSitesTransformation(AbstractTransformation):
"""
This transformation substitutes certain sites with certain species.
"""
def __init__(self, species, coords, coords_are_cartesian=False, validate_proximity=True):
"""
Args:
species: A list of species. e.g., ["Li", "Fe"]
coords: A list of coords corresponding to those species. e.g.,
[[0,0,0],[0.5,0.5,0.5]].
coords_are_cartesian (bool): Set to True if coords are given in
Cartesian coords. Defaults to False.
validate_proximity (bool): Set to False if you do not wish to ensure
that added sites are not too close to other sites. Defaults to True.
"""
if len(species) != len(coords):
raise ValueError("Species and coords must be the same length!")
self.species = species
self.coords = coords
self.coords_are_cartesian = coords_are_cartesian
self.validate_proximity = validate_proximity
def apply_transformation(self, structure):
"""
Apply the transformation.
Arg:
structure (Structure): A structurally similar structure in
regards to crystal and site positions.
Return:
Returns a copy of structure with sites inserted.
"""
s = structure.copy()
for i, sp in enumerate(self.species):
s.insert(
i,
sp,
self.coords[i],
coords_are_cartesian=self.coords_are_cartesian,
validate_proximity=self.validate_proximity,
)
return s.get_sorted_structure()
def __str__(self):
return f"InsertSiteTransformation : species {self.species}, coords {self.coords}"
def __repr__(self):
return str(self)
@property
def inverse(self):
"""Return: None"""
return None
@property
def is_one_to_many(self):
"""Return: False"""
return False
class ReplaceSiteSpeciesTransformation(AbstractTransformation):
"""
This transformation substitutes certain sites with certain species.
"""
def __init__(self, indices_species_map):
"""
Args:
indices_species_map: A dict containing the species mapping in
int-string pairs. E.g., { 1:"Na"} or {2:"Mn2+"}. Multiple
substitutions can be done. Overloaded to accept sp_and_occu
dictionary. E.g. {1: {"Ge":0.75, "C":0.25} }, which
substitutes a single species with multiple species to generate a
disordered structure.
"""
self.indices_species_map = indices_species_map
def apply_transformation(self, structure):
"""
Apply the transformation.
Arg:
structure (Structure): A structurally similar structure in
regards to crystal and site positions.
Return:
Returns a copy of structure with sites replaced.
"""
s = structure.copy()
for i, sp in self.indices_species_map.items():
s[int(i)] = sp
return s
def __str__(self):
return "ReplaceSiteSpeciesTransformation :" + ", ".join(
[f"{k}->{v}" + v for k, v in self.indices_species_map.items()]
)
def __repr__(self):
return str(self)
@property
def inverse(self):
"""Return: None"""
return None
@property
def is_one_to_many(self):
"""Return: False"""
return False
class RemoveSitesTransformation(AbstractTransformation):
"""
Remove certain sites in a structure.
"""
def __init__(self, indices_to_remove):
"""
Args:
indices_to_remove: List of indices to remove. E.g., [0, 1, 2]
"""
self.indices_to_remove = indices_to_remove
def apply_transformation(self, structure):
"""
Apply the transformation.
Arg:
structure (Structure): A structurally similar structure in
regards to crystal and site positions.
Return:
Returns a copy of structure with sites removed.
"""
s = structure.copy()
s.remove_sites(self.indices_to_remove)
return s
def __str__(self):
return "RemoveSitesTransformation :" + ", ".join(map(str, self.indices_to_remove))
def __repr__(self):
return str(self)
@property
def inverse(self):
"""Return: None"""
return None
@property
def is_one_to_many(self):
"""Return: False"""
return False
class TranslateSitesTransformation(AbstractTransformation):
"""
This class translates a set of sites by a certain vector.
"""
def __init__(self, indices_to_move, translation_vector, vector_in_frac_coords=True):
"""
Args:
indices_to_move: The indices of the sites to move
translation_vector: Vector to move the sites. If a list of list or numpy
array of shape, (len(indices_to_move), 3), is provided then each
translation vector is applied to the corresponding site in the
indices_to_move.
vector_in_frac_coords: Set to True if the translation vector is in
fractional coordinates, and False if it is in cartesian
coordinations. Defaults to True.
"""
self.indices_to_move = indices_to_move
self.translation_vector = np.array(translation_vector)
self.vector_in_frac_coords = vector_in_frac_coords
def apply_transformation(self, structure):
"""
Apply the transformation.
Arg:
structure (Structure): A structurally similar structure in
regards to crystal and site positions.
Return:
Returns a copy of structure with sites translated.
"""
s = structure.copy()
if self.translation_vector.shape == (len(self.indices_to_move), 3):
for i, idx in enumerate(self.indices_to_move):
s.translate_sites(idx, self.translation_vector[i], self.vector_in_frac_coords)
else:
s.translate_sites(self.indices_to_move, self.translation_vector, self.vector_in_frac_coords)
return s
def __str__(self):
return (
f"TranslateSitesTransformation for indices {self.indices_to_move}, "
f"vect {self.translation_vector} and "
f"vect_in_frac_coords = {self.vector_in_frac_coords}"
)
def __repr__(self):
return str(self)
@property
def inverse(self):
"""
Returns:
TranslateSitesTransformation with the reverse translation.
"""
return TranslateSitesTransformation(self.indices_to_move, -self.translation_vector, self.vector_in_frac_coords)
@property
def is_one_to_many(self):
"""Return: False"""
return False
def as_dict(self):
"""
JSON-serializable dict representation.
"""
d = MSONable.as_dict(self)
d["translation_vector"] = self.translation_vector.tolist()
return d
class PartialRemoveSitesTransformation(AbstractTransformation):
"""
Remove fraction of specie from a structure.
Requires an oxidation state decorated structure for Ewald sum to be
computed.
Given that the solution to selecting the right removals is NP-hard, there
are several algorithms provided with varying degrees of accuracy and speed.
The options are as follows:
ALGO_FAST:
This is a highly optimized algorithm to quickly go through the search
tree. It is guaranteed to find the optimal solution, but will return
only a single lowest energy structure. Typically, you will want to use
this.
ALGO_COMPLETE:
The complete algo ensures that you get all symmetrically distinct
orderings, ranked by the estimated Ewald energy. But this can be an
extremely time-consuming process if the number of possible orderings is
very large. Use this if you really want all possible orderings. If you
want just the lowest energy ordering, ALGO_FAST is accurate and faster.
ALGO_BEST_FIRST:
This algorithm is for ordering the really large cells that defeats even
ALGO_FAST. For example, if you have 48 sites of which you want to
remove 16 of them, the number of possible orderings is around
2 x 10^12. ALGO_BEST_FIRST shortcircuits the entire search tree by
removing the highest energy site first, then followed by the next
highest energy site, and so on. It is guaranteed to find a solution
in a reasonable time, but it is also likely to be highly inaccurate.
ALGO_ENUMERATE:
This algorithm uses the EnumerateStructureTransformation to perform
ordering. This algo returns *complete* orderings up to a single unit
cell size. It is more robust than the ALGO_COMPLETE, but requires
Gus Hart's enumlib to be installed.
"""
ALGO_FAST = 0
ALGO_COMPLETE = 1
ALGO_BEST_FIRST = 2
ALGO_ENUMERATE = 3
def __init__(self, indices, fractions, algo=ALGO_COMPLETE):
"""
Args:
indices:
A list of list of indices.
e.g. [[0, 1], [2, 3, 4, 5]]
fractions:
The corresponding fractions to remove. Must be same length as
indices. e.g., [0.5, 0.25]
algo:
This parameter allows you to choose the algorithm to perform
ordering. Use one of PartialRemoveSpecieTransformation.ALGO_*
variables to set the algo.
"""
self.indices = indices
self.fractions = fractions
self.algo = algo
self.logger = logging.getLogger(type(self).__name__)
def _best_first_ordering(self, structure: Structure, num_remove_dict):
self.logger.debug("Performing best first ordering")
start_time = time.perf_counter()
self.logger.debug("Performing initial Ewald sum...")
ewaldsum = EwaldSummation(structure)
self.logger.debug(f"Ewald sum took {time.perf_counter() - start_time} seconds.")
start_time = time.perf_counter()
ematrix = ewaldsum.total_energy_matrix
to_delete = []
totalremovals = sum(num_remove_dict.values())
removed = {k: 0 for k in num_remove_dict}
for _ in range(totalremovals):
max_idx = None
maxe = float("-inf")
maxindices = None
for indices in num_remove_dict:
if removed[indices] < num_remove_dict[indices]:
for ind in indices:
if ind not in to_delete:
energy = sum(ematrix[:, ind]) + sum(ematrix[:, ind]) - ematrix[ind, ind]
if energy > maxe:
max_idx = ind
maxe = energy
maxindices = indices
removed[maxindices] += 1
to_delete.append(max_idx)
ematrix[:, max_idx] = 0
ematrix[max_idx, :] = 0
s = structure.copy()
s.remove_sites(to_delete)
self.logger.debug(f"Minimizing Ewald took {time.perf_counter() - start_time} seconds.")
return [{"energy": sum(ematrix), "structure": s.get_sorted_structure()}]
def _complete_ordering(self, structure: Structure, num_remove_dict):
self.logger.debug("Performing complete ordering...")
all_structures: list[dict[str, float | Structure]] = []
symprec = 0.2
s = SpacegroupAnalyzer(structure, symprec=symprec)
self.logger.debug(f"Symmetry of structure is determined to be {s.get_space_group_symbol()}.")
sg = s.get_space_group_operations()
tested_sites: list[list[PeriodicSite]] = []
start_time = time.perf_counter()
self.logger.debug("Performing initial Ewald sum...")
ewaldsum = EwaldSummation(structure)
self.logger.debug(f"Ewald sum took {time.perf_counter() - start_time} seconds.")
start_time = time.perf_counter()
allcombis = []
for ind, num in num_remove_dict.items():
allcombis.append(itertools.combinations(ind, num))
count = 0
for allindices in itertools.product(*allcombis):
sites_to_remove = []
indices_list = []
for indices in allindices:
sites_to_remove.extend([structure[i] for i in indices])
indices_list.extend(indices)
s_new = structure.copy()
s_new.remove_sites(indices_list)
energy = ewaldsum.compute_partial_energy(indices_list)
already_tested = False
for i, tsites in enumerate(tested_sites):
tenergy = all_structures[i]["energy"]
if abs((energy - tenergy) / len(s_new)) < 1e-5 and sg.are_symmetrically_equivalent(
sites_to_remove, tsites, symm_prec=symprec
):
already_tested = True
if not already_tested:
tested_sites.append(sites_to_remove)
all_structures.append({"structure": s_new, "energy": energy})
count += 1
if count % 10 == 0:
timenow = time.perf_counter()
self.logger.debug(f"{count} structures, {timenow - start_time:.2f} seconds.")
self.logger.debug(f"Average time per combi = {(timenow - start_time) / count} seconds")
self.logger.debug(f"{len(all_structures)} symmetrically distinct structures found.")
self.logger.debug(f"Total symmetrically distinct structures found = {len(all_structures)}")
all_structures = sorted(all_structures, key=lambda s: s["energy"])
return all_structures
def _fast_ordering(self, structure: Structure, num_remove_dict, num_to_return=1):
"""
This method uses the matrix form of ewaldsum to calculate the ewald
sums of the potential structures. This is on the order of 4 orders of
magnitude faster when there are large numbers of permutations to
consider. There are further optimizations possible (doing a smarter
search of permutations for example), but this won't make a difference
until the number of permutations is on the order of 30,000.
"""
self.logger.debug("Performing fast ordering")
start_time = time.perf_counter()
self.logger.debug("Performing initial Ewald sum...")
ewaldmatrix = EwaldSummation(structure).total_energy_matrix
self.logger.debug(f"Ewald sum took {time.perf_counter() - start_time} seconds.")
start_time = time.perf_counter()
m_list = []
for indices, num in num_remove_dict.items():
m_list.append([0, num, list(indices), None])
self.logger.debug("Calling EwaldMinimizer...")
minimizer = EwaldMinimizer(ewaldmatrix, m_list, num_to_return, PartialRemoveSitesTransformation.ALGO_FAST)
self.logger.debug(f"Minimizing Ewald took {time.perf_counter() - start_time} seconds.")
all_structures = []
lowest_energy = minimizer.output_lists[0][0]
num_atoms = sum(structure.composition.values())
for output in minimizer.output_lists:
s = structure.copy()
del_indices = []
for manipulation in output[1]:
if manipulation[1] is None:
del_indices.append(manipulation[0])
else:
s.replace(manipulation[0], manipulation[1])
s.remove_sites(del_indices)
struct = s.get_sorted_structure()
all_structures.append(
{
"energy": output[0],
"energy_above_minimum": (output[0] - lowest_energy) / num_atoms,
"structure": struct,
}
)
return all_structures
def _enumerate_ordering(self, structure):
# Generate the disordered structure first.
s = structure.copy()
for indices, fraction in zip(self.indices, self.fractions):
for ind in indices:
new_sp = {sp: occu * fraction for sp, occu in structure[ind].species.items()}
s[ind] = new_sp
# Perform enumeration
from pymatgen.transformations.advanced_transformations import (
EnumerateStructureTransformation,
)
trans = EnumerateStructureTransformation()
return trans.apply_transformation(s, 10000)
def apply_transformation(self, structure: Structure, return_ranked_list=False):
"""
Apply the transformation.
Args:
structure: input structure
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 key "transformation" is reserved for the transformation that
was actually applied to the structure.
This transformation is parsed by the alchemy classes for generating
a more specific transformation history. Any other information will
be stored in the transformation_parameters dictionary in the
transmuted structure class.
"""
num_remove_dict = {}
total_combis = 0
for indices, frac in zip(self.indices, self.fractions):
num_to_remove = len(indices) * frac
if abs(num_to_remove - int(round(num_to_remove))) > 1e-3:
raise ValueError("Fraction to remove must be consistent with integer amounts in structure.")
num_to_remove = int(round(num_to_remove))
num_remove_dict[tuple(indices)] = num_to_remove
n = len(indices)
total_combis += int(
round(math.factorial(n) / math.factorial(num_to_remove) / math.factorial(n - num_to_remove))
)
self.logger.debug(f"Total combinations = {total_combis}")
try:
num_to_return = int(return_ranked_list)
except ValueError:
num_to_return = 1
num_to_return = max(1, num_to_return)
self.logger.debug(f"Will return {num_to_return} best structures.")
if self.algo == PartialRemoveSitesTransformation.ALGO_FAST:
all_structures = self._fast_ordering(structure, num_remove_dict, num_to_return)
elif self.algo == PartialRemoveSitesTransformation.ALGO_COMPLETE:
all_structures = self._complete_ordering(structure, num_remove_dict)
elif self.algo == PartialRemoveSitesTransformation.ALGO_BEST_FIRST:
all_structures = self._best_first_ordering(structure, num_remove_dict)
elif self.algo == PartialRemoveSitesTransformation.ALGO_ENUMERATE:
all_structures = self._enumerate_ordering(structure)
else:
raise ValueError("Invalid algo.")
opt_s = all_structures[0]["structure"]
return opt_s if not return_ranked_list else all_structures[0:num_to_return]
def __str__(self):
return f"PartialRemoveSitesTransformation : Indices and fraction to remove = {self.indices}, ALGO = {self.algo}"
def __repr__(self):
return str(self)
@property
def inverse(self):
"""Return: None"""
return None
@property
def is_one_to_many(self):
"""Return: True"""
return True
class AddSitePropertyTransformation(AbstractTransformation):
"""
Simple transformation to add site properties to a given structure
"""
def __init__(self, site_properties):
"""
Args:
site_properties (dict): site properties to be added to a structure
"""
self.site_properties = site_properties
def apply_transformation(self, structure):
"""
Apply the transformation.
Arg:
structure (Structure): A structurally similar structure in
regards to crystal and site positions.
Return:
Returns a copy of structure with sites properties added.
"""
new_structure = structure.copy()
for prop in self.site_properties:
new_structure.add_site_property(prop, self.site_properties[prop])
return new_structure
@property
def inverse(self):
"""Return: None"""
return None
@property
def is_one_to_many(self):
"""Return: False"""
return False
class RadialSiteDistortionTransformation(AbstractTransformation):
"""
Radially perturbs atoms around a site. Can be used to create spherical distortion due to a
point defect.
"""
def __init__(self, site_index, displacement=0.1, nn_only=False):
"""
Args:
site_index (int): index of the site in structure to place at the center of the distortion (will
not be distorted). This index must be provided before the structure is provided in
apply_transformation in order to keep in line with the base class.
displacement (float): distance to perturb the atoms around the objective site
nn_only (bool): Whether or not to perturb beyond the nearest neighbors. If True, then only the
nearest neighbors will be perturbed, leaving the other sites undisturbed. If False, then
the nearest neighbors will receive the full displacement, and then subsequent sites will receive
a displacement=0.1 / r, where r is the distance each site to the origin site. For small displacements,
atoms beyond the NN environment will receive very small displacements, and these are almost equal.
For large displacements, this difference is noticeable.
"""
self.site_index = site_index
self.displacement = displacement
self.nn_only = nn_only
def apply_transformation(self, structure):
"""
Apply the transformation.
Args:
structure: Structure or Molecule to apply the transformation to
Returns:
the transformed structure
"""
structure = structure.copy()
site = structure[self.site_index]
def f(x, r, r0):
return x * r0 / r
r0 = max(site.distance(_["site"]) for _ in MinimumDistanceNN().get_nn_info(structure, self.site_index))
if hasattr(structure, "lattice"):
m = structure.lattice.matrix
m = (abs(m) > 1e-5) * m
a, b, c = m[0], m[1], m[2]
x = abs(np.dot(a, np.cross(b, c)) / np.linalg.norm(np.cross(b, c)))
y = abs(np.dot(b, np.cross(a, c)) / np.linalg.norm(np.cross(a, c)))
z = abs(np.dot(c, np.cross(a, b)) / np.linalg.norm(np.cross(a, b)))
rmax = np.floor(min([x, y, z]) / 2)
else:
rmax = np.max(structure.distance_matrix)
for vals in structure.get_neighbors(site, r=r0 if self.nn_only else rmax):
site2, distance, index = vals[:3]
v = site2.coords - site.coords
kwargs = {"indices": [index], "vector": v * f(self.displacement, distance, r0) / np.linalg.norm(v)}
if hasattr(structure, "lattice"):
kwargs["frac_coords"] = False
structure.translate_sites(**kwargs)
return structure
@property
def inverse(self):
"""
Returns the inverse transformation if available.
Otherwise, should return None.
"""
return False
@property
def is_one_to_many(self):
"""
Determines if a Transformation is a one-to-many transformation. If a
Transformation is a one-to-many transformation, the
apply_transformation method should have a keyword arg
"return_ranked_list" which allows for the transformed structures to be
returned as a ranked list.
"""
return False
@property
def use_multiprocessing(self):
"""
Indicates whether the transformation can be applied by a
subprocessing pool. This should be overridden to return True for
transformations that the transmuter can parallelize.
"""
return False