Source code for pymatgen.transformations.advanced_transformations
# coding: utf-8
# Copyright (c) Pymatgen Development Team.
# Distributed under the terms of the MIT License.
from __future__ import division, unicode_literals
import numpy as np
from fractions import Fraction
try:
from math import gcd
except ImportError:
from fractions import gcd
from itertools import groupby, product
from string import ascii_lowercase
from warnings import warn
import logging
import math
import warnings
from monty.fractions import lcm
from monty.json import MSONable
from pymatgen.core.periodic_table import Element, Specie, get_el_sp, DummySpecie
from pymatgen.transformations.transformation_abc import AbstractTransformation
from pymatgen.transformations.standard_transformations import \
SubstitutionTransformation, OrderDisorderedStructureTransformation
from pymatgen.command_line.enumlib_caller import EnumlibAdaptor, EnumError
from pymatgen.analysis.ewald import EwaldSummation
from pymatgen.core.structure import Structure
from pymatgen.symmetry.analyzer import SpacegroupAnalyzer
from pymatgen.analysis.structure_prediction.substitution_probability import \
SubstitutionPredictor
from pymatgen.analysis.structure_matcher import StructureMatcher, \
SpinComparator
from pymatgen.analysis.energy_models import SymmetryModel
from pymatgen.analysis.bond_valence import BVAnalyzer
from pymatgen.core.surface import SlabGenerator
from pymatgen.electronic_structure.core import Spin
from pymatgen.analysis.gb.gb import GBGenerator
"""
This module implements more advanced transformations.
"""
__author__ = "Shyue Ping Ong, Stephen Dacek, Anubhav Jain, Matthew Horton"
__copyright__ = "Copyright 2012, The Materials Project"
__version__ = "1.0"
__maintainer__ = "Shyue Ping Ong"
__email__ = "shyuep@gmail.com"
__date__ = "Jul 24, 2012"
logger = logging.getLogger(__name__)
[docs]class ChargeBalanceTransformation(AbstractTransformation):
"""
This is a transformation that disorders a structure to make it charge
balanced, given an oxidation state-decorated structure.
Args:
charge_balance_sp: specie to add or remove. Currently only removal
is supported
"""
def __init__(self, charge_balance_sp):
self.charge_balance_sp = str(charge_balance_sp)
[docs] def apply_transformation(self, structure):
charge = structure.charge
specie = get_el_sp(self.charge_balance_sp)
num_to_remove = charge / specie.oxi_state
num_in_structure = structure.composition[specie]
removal_fraction = num_to_remove / num_in_structure
if removal_fraction < 0:
raise ValueError("addition of specie not yet supported by "
"ChargeBalanceTransformation")
trans = SubstitutionTransformation(
{self.charge_balance_sp: {
self.charge_balance_sp: 1 - removal_fraction}})
return trans.apply_transformation(structure)
def __str__(self):
return "Charge Balance Transformation : " + \
"Species to remove = {}".format(str(self.charge_balance_sp))
def __repr__(self):
return self.__str__()
@property
def inverse(self):
return None
@property
def is_one_to_many(self):
return False
[docs]class SuperTransformation(AbstractTransformation):
"""
This is a transformation that is inherently one-to-many. It is constructed
from a list of transformations and returns one structure for each
transformation. The primary use for this class is extending a transmuter
object.
Args:
transformations ([transformations]): List of transformations to apply
to a structure. One transformation is applied to each output
structure.
nstructures_per_trans (int): If the transformations are one-to-many and,
nstructures_per_trans structures from each transformation are
added to the full list. Defaults to 1, i.e., only best structure.
"""
def __init__(self, transformations, nstructures_per_trans=1):
self._transformations = transformations
self.nstructures_per_trans = nstructures_per_trans
[docs] def apply_transformation(self, structure, return_ranked_list=False):
if not return_ranked_list:
raise ValueError("SuperTransformation has no single best structure"
" output. Must use return_ranked_list")
structures = []
for t in self._transformations:
if t.is_one_to_many:
for d in t.apply_transformation(
structure,
return_ranked_list=self.nstructures_per_trans):
d["transformation"] = t
structures.append(d)
else:
structures.append(
{"transformation": t,
"structure": t.apply_transformation(structure)})
return structures
def __str__(self):
return "Super Transformation : Transformations = " + \
"{}".format(" ".join([str(t) for t in self._transformations]))
def __repr__(self):
return self.__str__()
@property
def inverse(self):
return None
@property
def is_one_to_many(self):
return True
[docs]class MultipleSubstitutionTransformation(object):
"""
Performs multiple substitutions on a structure. For example, can do a
fractional replacement of Ge in LiGePS with a list of species, creating one
structure for each substitution. Ordering is done using a dummy element so
only one ordering must be done per substitution oxidation state. Charge
balancing of the structure is optionally performed.
.. note::
There are no checks to make sure that removal fractions are possible
and rounding may occur. Currently charge balancing only works for
removal of species.
"""
def __init__(self, sp_to_replace, r_fraction, substitution_dict,
charge_balance_species=None, order=True):
"""
Performs multiple fractional substitutions on a transmuter.
Args:
sp_to_replace: species to be replaced
r_fraction: fraction of that specie to replace
substitution_dict: dictionary of the format
{2: ["Mg", "Ti", "V", "As", "Cr", "Ta", "N", "Nb"],
3: ["Ru", "Fe", "Co", "Ce", "As", "Cr", "Ta", "N", "Nb"],
4: ["Ru", "V", "Cr", "Ta", "N", "Nb"],
5: ["Ru", "W", "Mn"]
}
The number is the charge used for each of the list of elements
(an element can be present in multiple lists)
charge_balance_species: If specified, will balance the charge on
the structure using that specie.
"""
self.sp_to_replace = sp_to_replace
self.r_fraction = r_fraction
self.substitution_dict = substitution_dict
self.charge_balance_species = charge_balance_species
self.order = order
[docs] def apply_transformation(self, structure, return_ranked_list=False):
if not return_ranked_list:
raise ValueError("MultipleSubstitutionTransformation has no single"
" best structure output. Must use"
" return_ranked_list.")
outputs = []
for charge, el_list in self.substitution_dict.items():
mapping = {}
if charge > 0:
sign = "+"
else:
sign = "-"
dummy_sp = "X{}{}".format(str(charge), sign)
mapping[self.sp_to_replace] = {
self.sp_to_replace: 1 - self.r_fraction,
dummy_sp: self.r_fraction}
trans = SubstitutionTransformation(mapping)
dummy_structure = trans.apply_transformation(structure)
if self.charge_balance_species is not None:
cbt = ChargeBalanceTransformation(self.charge_balance_species)
dummy_structure = cbt.apply_transformation(dummy_structure)
if self.order:
trans = OrderDisorderedStructureTransformation()
dummy_structure = trans.apply_transformation(dummy_structure)
for el in el_list:
if charge > 0:
sign = "+"
else:
sign = "-"
st = SubstitutionTransformation(
{"X{}+".format(str(charge)): "{}{}{}".format(el, charge,
sign)})
new_structure = st.apply_transformation(dummy_structure)
outputs.append({"structure": new_structure})
return outputs
def __str__(self):
return "Multiple Substitution Transformation : Substitution on " + \
"{}".format(self.sp_to_replace)
def __repr__(self):
return self.__str__()
@property
def inverse(self):
return None
@property
def is_one_to_many(self):
return True
[docs]class EnumerateStructureTransformation(AbstractTransformation):
"""
Order a disordered structure using enumlib. For complete orderings, this
generally produces fewer structures that the OrderDisorderedStructure
transformation, and at a much faster speed.
Args:
min_cell_size:
The minimum cell size wanted. Must be an int. Defaults to 1.
max_cell_size:
The maximum cell size wanted. Must be an int. Defaults to 1.
symm_prec:
Tolerance to use for symmetry.
refine_structure:
This parameter has the same meaning as in enumlib_caller.
If you are starting from a structure that has been relaxed via
some electronic structure code, it is usually much better to
start with symmetry determination and then obtain a refined
structure. The refined structure have cell parameters and
atomic positions shifted to the expected symmetry positions,
which makes it much less sensitive precision issues in enumlib.
If you are already starting from an experimental cif, refinment
should have already been done and it is not necessary. Defaults
to False.
enum_precision_parameter (float): Finite precision parameter for
enumlib. Default of 0.001 is usually ok, but you might need to
tweak it for certain cells.
check_ordered_symmetry (bool): Whether to check the symmetry of
the ordered sites. If the symmetry of the ordered sites is
lower, the lowest symmetry ordered sites is included in the
enumeration. This is important if the ordered sites break
symmetry in a way that is important getting possible
structures. But sometimes including ordered sites
slows down enumeration to the point that it cannot be
completed. Switch to False in those cases. Defaults to True.
max_disordered_sites (int):
An alternate parameter to max_cell size. Will sequentially try
larger and larger cell sizes until (i) getting a result or (ii)
the number of disordered sites in the cell exceeds
max_disordered_sites. Must set max_cell_size to None when using
this parameter.
sort_criteria (str): Sort by Ewald energy ("ewald", must have oxidation
states and slow) or by number of sites ("nsites", much faster).
timeout (float): timeout in minutes to pass to EnumlibAdaptor
"""
def __init__(self, min_cell_size=1, max_cell_size=1, symm_prec=0.1,
refine_structure=False, enum_precision_parameter=0.001,
check_ordered_symmetry=True, max_disordered_sites=None,
sort_criteria="ewald", timeout=None):
self.symm_prec = symm_prec
self.min_cell_size = min_cell_size
self.max_cell_size = max_cell_size
self.refine_structure = refine_structure
self.enum_precision_parameter = enum_precision_parameter
self.check_ordered_symmetry = check_ordered_symmetry
self.max_disordered_sites = max_disordered_sites
self.sort_criteria = sort_criteria
self.timeout = timeout
if max_cell_size and max_disordered_sites:
raise ValueError("Cannot set both max_cell_size and "
"max_disordered_sites!")
[docs] def apply_transformation(self, structure, return_ranked_list=False):
"""
Return either a single ordered structure or a sequence of all ordered
structures.
Args:
structure: Structure to order.
return_ranked_list (bool): Whether or not multiple structures are
returned. If return_ranked_list is a number, that number of
structures is returned.
Returns:
Depending on returned_ranked list, either a transformed structure
or a list of dictionaries, where each dictionary is of the form
{"structure" = .... , "other_arguments"}
The list of ordered structures is ranked by ewald energy / atom, if
the input structure is an oxidation state decorated structure.
Otherwise, it is ranked by number of sites, with smallest number of
sites first.
"""
try:
num_to_return = int(return_ranked_list)
except ValueError:
num_to_return = 1
if self.refine_structure:
finder = SpacegroupAnalyzer(structure, self.symm_prec)
structure = finder.get_refined_structure()
contains_oxidation_state = all(
[hasattr(sp, "oxi_state") and sp.oxi_state != 0 for sp in
structure.composition.elements]
)
structures = None
if structure.is_ordered:
warn("Enumeration skipped for structure with composition {} "
"because it is ordered".format(structure.composition))
structures = [structure.copy()]
if self.max_disordered_sites:
ndisordered = sum([1 for site in structure if not site.is_ordered])
if ndisordered > self.max_disordered_sites:
raise ValueError(
"Too many disordered sites! ({} > {})".format(
ndisordered, self.max_disordered_sites))
max_cell_sizes = range(self.min_cell_size, int(
math.floor(self.max_disordered_sites / ndisordered)) + 1)
else:
max_cell_sizes = [self.max_cell_size]
for max_cell_size in max_cell_sizes:
adaptor = EnumlibAdaptor(
structure, min_cell_size=self.min_cell_size,
max_cell_size=max_cell_size,
symm_prec=self.symm_prec, refine_structure=False,
enum_precision_parameter=self.enum_precision_parameter,
check_ordered_symmetry=self.check_ordered_symmetry,
timeout=self.timeout)
try:
adaptor.run()
except EnumError:
warn("Unable to enumerate for max_cell_size = %d".format(
max_cell_size))
structures = adaptor.structures
if structures:
break
if structures is None:
raise ValueError("Unable to enumerate")
original_latt = structure.lattice
inv_latt = np.linalg.inv(original_latt.matrix)
ewald_matrices = {}
all_structures = []
for s in structures:
new_latt = s.lattice
transformation = np.dot(new_latt.matrix, inv_latt)
transformation = tuple([tuple([int(round(cell)) for cell in row])
for row in transformation])
if contains_oxidation_state and self.sort_criteria == "ewald":
if transformation not in ewald_matrices:
s_supercell = structure * transformation
ewald = EwaldSummation(s_supercell)
ewald_matrices[transformation] = ewald
else:
ewald = ewald_matrices[transformation]
energy = ewald.compute_sub_structure(s)
all_structures.append({"num_sites": len(s), "energy": energy,
"structure": s})
else:
all_structures.append({"num_sites": len(s), "structure": s})
def sort_func(s):
return s["energy"] / s["num_sites"] \
if contains_oxidation_state and self.sort_criteria == "ewald" \
else s["num_sites"]
self._all_structures = sorted(all_structures, key=sort_func)
if return_ranked_list:
return self._all_structures[0:num_to_return]
else:
return self._all_structures[0]["structure"]
def __str__(self):
return "EnumerateStructureTransformation"
def __repr__(self):
return self.__str__()
@property
def inverse(self):
return None
@property
def is_one_to_many(self):
return True
[docs]class SubstitutionPredictorTransformation(AbstractTransformation):
"""
This transformation takes a structure and uses the structure
prediction module to find likely site substitutions.
Args:
threshold: Threshold for substitution.
**kwargs: Args for SubstitutionProbability class lambda_table, alpha
"""
def __init__(self, threshold=1e-2, scale_volumes=True, **kwargs):
self.kwargs = kwargs
self.threshold = threshold
self.scale_volumes = scale_volumes
self._substitutor = SubstitutionPredictor(threshold=threshold,
**kwargs)
[docs] def apply_transformation(self, structure, return_ranked_list=False):
if not return_ranked_list:
raise ValueError("SubstitutionPredictorTransformation doesn't"
" support returning 1 structure")
preds = self._substitutor.composition_prediction(
structure.composition, to_this_composition=False)
preds.sort(key=lambda x: x['probability'], reverse=True)
outputs = []
for pred in preds:
st = SubstitutionTransformation(pred['substitutions'])
output = {'structure': st.apply_transformation(structure),
'probability': pred['probability'],
'threshold': self.threshold, 'substitutions': {}}
# dictionary keys have to be converted to strings for JSON
for key, value in pred['substitutions'].items():
output['substitutions'][str(key)] = str(value)
outputs.append(output)
return outputs
def __str__(self):
return "SubstitutionPredictorTransformation"
def __repr__(self):
return self.__str__()
@property
def inverse(self):
return None
@property
def is_one_to_many(self):
return True
[docs]class MagOrderParameterConstraint(MSONable):
def __init__(self, order_parameter,
species_constraints=None,
site_constraint_name=None,
site_constraints=None):
"""
This class can be used to supply MagOrderingTransformation
to just a specific subset of species or sites that satisfy the
provided constraints. This can be useful for setting an order
parameters for, for example, ferrimagnetic structures which
might order on certain motifs, with the global order parameter
dependent on how many sites satisfy that motif.
:param order_parameter (float): any number from 0.0 to 1.0,
typically 0.5 (antiferromagnetic) or 1.0 (ferromagnetic)
:param species_constraint (list): str or list of strings
of Specie symbols that the constraint should apply to
:param site_constraint_name (str): name of the site property
that the constraint should apply to, e.g. "coordination_no"
:param site_constraints (list): list of values of the site
property that the constraints should apply to
"""
# validation
if site_constraints and site_constraints != [None] \
and not site_constraint_name:
raise ValueError("Specify the name of the site constraint.")
elif not site_constraints and site_constraint_name:
raise ValueError("Please specify some site constraints.")
if not isinstance(species_constraints, list):
species_constraints = [species_constraints]
if not isinstance(site_constraints, list):
site_constraints = [site_constraints]
if order_parameter > 1 or order_parameter < 0:
raise ValueError('Order parameter must lie between 0 and 1')
elif order_parameter != 0.5:
warnings.warn("Use care when using a non-standard order parameter, "
"though it can be useful in some cases it can also "
"lead to unintended behavior. Consult documentation.")
self.order_parameter = order_parameter
self.species_constraints = species_constraints
self.site_constraint_name = site_constraint_name
self.site_constraints = site_constraints
[docs] def satisfies_constraint(self, site):
"""
Checks if a periodic site satisfies the constraint.
"""
if not site.is_ordered:
return False
if self.species_constraints \
and str(site.specie) in self.species_constraints:
satisfies_constraints = True
else:
satisfies_constraints = False
if self.site_constraint_name \
and self.site_constraint_name in site.properties:
prop = site.properties[self.site_constraint_name]
if prop in self.site_constraints:
satisfies_constraints = True
else:
satisfies_constraints = False
return satisfies_constraints
[docs]class MagOrderingTransformation(AbstractTransformation):
def __init__(self, mag_species_spin, order_parameter=0.5,
energy_model=SymmetryModel(), **kwargs):
"""
This transformation takes a structure and returns a list of collinear
magnetic orderings. For disordered structures, make an ordered
approximation first.
:param mag_species_spin: A mapping of elements/species to their
spin magnitudes, e.g. {"Fe3+": 5, "Mn3+": 4}
:param order_parameter (float or list): if float, a specifies a
global order parameter and can take values from 0.0 to 1.0
(e.g. 0.5 for antiferromagnetic or 1.0 for ferromagnetic), if
list has to be a list of
:class: `pymatgen.transformations.advanced_transformations.MagOrderParameterConstraint`
to specify more complicated orderings, see documentation for
MagOrderParameterConstraint more details on usage
:param energy_model: Energy model to rank the returned structures,
see :mod: `pymatgen.analysis.energy_models` for more information (note
that this is not necessarily a physical energy). By default, returned
structures use SymmetryModel() which ranks structures from most
symmetric to least.
:param kwargs: Additional kwargs that are passed to
:class:`EnumerateStructureTransformation` such as min_cell_size etc.
"""
# checking for sensible order_parameter values
if isinstance(order_parameter, float):
# convert to constraint format
order_parameter = [MagOrderParameterConstraint(order_parameter=order_parameter,
species_constraints=
list(mag_species_spin.keys()))]
elif isinstance(order_parameter, list):
ops = [isinstance(item, MagOrderParameterConstraint) for item in order_parameter]
if not any(ops):
raise ValueError("Order parameter not correctly defined.")
else:
raise ValueError("Order parameter not correctly defined.")
self.mag_species_spin = mag_species_spin
# store order parameter constraints as dicts to save implementing
# to/from dict methods for MSONable compatibility
self.order_parameter = [op.as_dict() for op in order_parameter]
self.energy_model = energy_model
self.enum_kwargs = kwargs
[docs] @staticmethod
def determine_min_cell(disordered_structure):
"""
Determine the smallest supercell that is able to enumerate
the provided structure with the given order parameter
"""
def lcm(n1, n2):
"""
Find least common multiple of two numbers
"""
return n1 * n2 / gcd(n1, n2)
# assumes all order parameters for a given species are the same
mag_species_order_parameter = {}
mag_species_occurrences = {}
for idx, site in enumerate(disordered_structure):
if not site.is_ordered:
op = max(site.species_and_occu.values())
# this very hacky bit of code only works because we know
# that on disordered sites in this class, all species are the same
# but have different spins, and this is comma-delimited
sp = str(list(site.species_and_occu.keys())[0]).split(",")[0]
if sp in mag_species_order_parameter:
mag_species_occurrences[sp] += 1
else:
mag_species_order_parameter[sp] = op
mag_species_occurrences[sp] = 1
smallest_n = []
for sp, order_parameter in mag_species_order_parameter.items():
denom = Fraction(order_parameter).limit_denominator(100).denominator
num_atom_per_specie = mag_species_occurrences[sp]
n_gcd = gcd(denom, num_atom_per_specie)
smallest_n.append(lcm(int(n_gcd), denom) / n_gcd)
return max(smallest_n)
@staticmethod
def _add_dummy_species(structure, order_parameters):
"""
:param structure: ordered Structure
:param order_parameters: list of MagOrderParameterConstraints
:return: A structure decorated with disordered
DummySpecies on which to perform the enumeration.
Note that the DummySpecies are super-imposed on
to the original sites, to make it easier to
retrieve the original site after enumeration is
performed (this approach is preferred over a simple
mapping since multiple species may have the same
DummySpecie, depending on the constraints specified).
This approach can also preserve site properties even after
enumeration.
"""
dummy_struct = structure.copy()
def generate_dummy_specie():
"""
Generator which returns DummySpecie symbols Mma, Mmb, etc.
"""
subscript_length = 1
while True:
for subscript in product(ascii_lowercase, repeat=subscript_length):
yield "Mm" + "".join(subscript)
subscript_length += 1
dummy_species_gen = generate_dummy_specie()
# one dummy species for each order parameter constraint
dummy_species_symbols = [next(dummy_species_gen) for i in range(len(order_parameters))]
dummy_species = [{
DummySpecie(symbol, properties={'spin': Spin.up}): constraint.order_parameter,
DummySpecie(symbol, properties={'spin': Spin.down}): 1 - constraint.order_parameter
} for symbol, constraint in zip(dummy_species_symbols, order_parameters)]
sites_to_add = []
for idx, site in enumerate(dummy_struct):
satisfies_constraints = [c.satisfies_constraint(site) for c in order_parameters]
if satisfies_constraints.count(True) > 1:
# site should either not satisfy any constraints, or satisfy
# one constraint
raise ValueError("Order parameter constraints conflict for site: {}, {}"
.format(str(site.specie), site.properties))
elif any(satisfies_constraints):
dummy_specie_idx = satisfies_constraints.index(True)
dummy_struct.append(
dummy_species[dummy_specie_idx],
site.coords,
site.lattice
)
return dummy_struct
@staticmethod
def _remove_dummy_species(structure):
"""
:return: Structure with dummy species removed, but
their corresponding spin properties merged with the
original sites. Used after performing enumeration.
"""
if not structure.is_ordered:
raise Exception("Something went wrong with enumeration.")
sites_to_remove = []
logger.debug('Dummy species structure:\n{}'.format(str(structure)))
for idx, site in enumerate(structure):
if isinstance(site.specie, DummySpecie):
sites_to_remove.append(idx)
spin = site.specie._properties.get('spin', None)
neighbors = structure.get_neighbors(
site,
0.05, # arbitrary threshold, needs to be << any bond length
# but >> floating point precision issues
include_index=True
)
if len(neighbors) != 1:
raise Exception("This shouldn't happen, found neighbors: {}"
.format(neighbors))
orig_site_idx = neighbors[0][2]
orig_specie = structure[orig_site_idx].specie
new_specie = Specie(orig_specie.symbol,
getattr(orig_specie, 'oxi_state', None),
properties={'spin': spin})
structure.replace(orig_site_idx,
new_specie,
properties=structure[orig_site_idx].properties)
structure.remove_sites(sites_to_remove)
logger.debug('Structure with dummy species removed:\n{}'.format(str(structure)))
return structure
def _add_spin_magnitudes(self, structure):
"""
Replaces Spin.up/Spin.down with spin magnitudes specified
by mag_species_spin.
:param structure:
:return:
"""
for idx, site in enumerate(structure):
if getattr(site.specie, '_properties', None):
spin = site.specie._properties.get('spin', None)
sign = int(spin) if spin else 0
if spin:
new_properties = site.specie._properties.copy()
# this very hacky bit of code only works because we know
# that on disordered sites in this class, all species are the same
# but have different spins, and this is comma-delimited
sp = str(site.specie).split(",")[0]
new_properties.update({
'spin': sign * self.mag_species_spin.get(sp, 0)
})
new_specie = Specie(site.specie.symbol,
getattr(site.specie, 'oxi_state', None),
new_properties)
structure.replace(idx, new_specie,
properties=site.properties)
logger.debug('Structure with spin magnitudes:\n{}'.format(str(structure)))
return structure
[docs] def apply_transformation(self, structure, return_ranked_list=False):
"""
Apply MagOrderTransformation to an input structure.
:param structure: Any ordered structure.
:param return_ranked_list: As in other Transformations.
:return:
"""
if not structure.is_ordered:
raise ValueError("Create an ordered approximation of "
"your input structure first.")
# retrieve order parameters
order_parameters = [MagOrderParameterConstraint.from_dict(op_dict)
for op_dict in self.order_parameter]
# add dummy species on which to perform enumeration
structure = self._add_dummy_species(structure, order_parameters)
# trivial case
if structure.is_ordered:
structure = self._remove_dummy_species(structure)
return [structure] if return_ranked_list > 1 else structure
enum_kwargs = self.enum_kwargs.copy()
enum_kwargs["min_cell_size"] = max(
int(self.determine_min_cell(structure)),
enum_kwargs.get("min_cell_size", 1)
)
if enum_kwargs.get("max_cell_size", None):
if enum_kwargs["min_cell_size"] > enum_kwargs["max_cell_size"]:
warnings.warn("Specified max cell size ({}) is smaller "
"than the minimum enumerable cell size ({}), "
"changing max cell size to {}".format(enum_kwargs["max_cell_size"],
enum_kwargs["min_cell_size"],
enum_kwargs["min_cell_size"]))
enum_kwargs["max_cell_size"] = enum_kwargs["min_cell_size"]
else:
enum_kwargs["max_cell_size"] = enum_kwargs["min_cell_size"]
t = EnumerateStructureTransformation(**enum_kwargs)
alls = t.apply_transformation(structure,
return_ranked_list=return_ranked_list)
# handle the fact that EnumerateStructureTransformation can either
# return a single Structure or a list
if isinstance(alls, Structure):
# remove dummy species and replace Spin.up or Spin.down
# with spin magnitudes given in mag_species_spin arg
alls = self._remove_dummy_species(alls)
alls = self._add_spin_magnitudes(alls)
else:
for idx, _ in enumerate(alls):
alls[idx]["structure"] = self._remove_dummy_species(alls[idx]["structure"])
alls[idx]["structure"] = self._add_spin_magnitudes(alls[idx]["structure"])
try:
num_to_return = int(return_ranked_list)
except ValueError:
num_to_return = 1
if num_to_return == 1 or not return_ranked_list:
return alls[0]["structure"] if num_to_return else alls
# remove duplicate structures and group according to energy model
m = StructureMatcher(comparator=SpinComparator())
key = lambda x: SpacegroupAnalyzer(x, 0.1).get_space_group_number()
out = []
for _, g in groupby(sorted([d["structure"] for d in alls],
key=key), key):
g = list(g)
grouped = m.group_structures(g)
out.extend([{"structure": g[0],
"energy": self.energy_model.get_energy(g[0])}
for g in grouped])
self._all_structures = sorted(out, key=lambda d: d["energy"])
return self._all_structures[0:num_to_return]
def __str__(self):
return "MagOrderingTransformation"
def __repr__(self):
return self.__str__()
@property
def inverse(self):
return None
@property
def is_one_to_many(self):
return True
def _find_codopant(target, oxidation_state, allowed_elements=None):
"""
Finds the element from "allowed elements" that (i) possesses the desired
"oxidation state" and (ii) is closest in ionic radius to the target specie
Args:
target: (Specie) provides target ionic radius.
oxidation_state: (float) codopant oxidation state.
allowed_elements: ([str]) List of allowed elements. If None,
all elements are tried.
Returns:
(Specie) with oxidation_state that has ionic radius closest to
target.
"""
ref_radius = target.ionic_radius
candidates = []
symbols = allowed_elements or [el.symbol for el in Element]
for sym in symbols:
try:
with warnings.catch_warnings():
warnings.simplefilter("ignore")
sp = Specie(sym, oxidation_state)
r = sp.ionic_radius
if r is not None:
candidates.append((r, sp))
except:
pass
return min(candidates, key=lambda l: abs(l[0] / ref_radius - 1))[1]
[docs]class DopingTransformation(AbstractTransformation):
"""
A transformation that performs doping of a structure.
"""
def __init__(self, dopant, ionic_radius_tol=float("inf"), min_length=10,
alio_tol=0, codopant=False, max_structures_per_enum=100,
allowed_doping_species=None, **kwargs):
"""
Args:
dopant (Specie-like): E.g., Al3+. Must have oxidation state.
ionic_radius_tol (float): E.g., Fractional allowable ionic radii
mismatch for dopant to fit into a site. Default of inf means
that any dopant with the right oxidation state is allowed.
min_Length (float): Min. lattice parameter between periodic
images of dopant. Defaults to 10A for now.
alio_tol (int): If this is not 0, attempt will be made to dope
sites with oxidation_states +- alio_tol of the dopant. E.g.,
1 means that the ions like Ca2+ and Ti4+ are considered as
potential doping sites for Al3+.
codopant (bool): If True, doping will be carried out with a
codopant to maintain charge neutrality. Otherwise, vacancies
will be used.
max_structures_per_enum (float): Maximum number of structures to
return per enumeration. Note that there can be more than one
candidate doping site, and each site enumeration will return at
max max_structures_per_enum structures. Defaults to 100.
allowed_doping_species (list): Species that are allowed to be
doping sites. This is an inclusionary list. If specified,
any sites which are not
\\*\\*kwargs:
Same keyword args as :class:`EnumerateStructureTransformation`,
i.e., min_cell_size, etc.
"""
self.dopant = get_el_sp(dopant)
self.ionic_radius_tol = ionic_radius_tol
self.min_length = min_length
self.alio_tol = alio_tol
self.codopant = codopant
self.max_structures_per_enum = max_structures_per_enum
self.allowed_doping_species = allowed_doping_species
self.kwargs = kwargs
[docs] def apply_transformation(self, structure, return_ranked_list=False):
"""
Args:
structure (Structure): Input structure to dope
Returns:
[{"structure": Structure, "energy": float}]
"""
comp = structure.composition
logger.info("Composition: %s" % comp)
for sp in comp:
try:
sp.oxi_state
except AttributeError:
analyzer = BVAnalyzer()
structure = analyzer.get_oxi_state_decorated_structure(
structure)
comp = structure.composition
break
ox = self.dopant.oxi_state
radius = self.dopant.ionic_radius
compatible_species = [
sp for sp in comp if sp.oxi_state == ox and
abs(sp.ionic_radius / radius - 1) < self.ionic_radius_tol]
if (not compatible_species) and self.alio_tol:
# We only consider aliovalent doping if there are no compatible
# isovalent species.
compatible_species = [
sp for sp in comp
if abs(sp.oxi_state - ox) <= self.alio_tol and
abs(sp.ionic_radius / radius - 1) < self.ionic_radius_tol and
sp.oxi_state * ox >= 0]
if self.allowed_doping_species is not None:
# Only keep allowed doping species.
compatible_species = [
sp for sp in compatible_species
if sp in [get_el_sp(s) for s in self.allowed_doping_species]]
logger.info("Compatible species: %s" % compatible_species)
lengths = structure.lattice.abc
scaling = [max(1, int(round(math.ceil(self.min_length / x))))
for x in lengths]
logger.info("Lengths are %s" % str(lengths))
logger.info("Scaling = %s" % str(scaling))
all_structures = []
t = EnumerateStructureTransformation(**self.kwargs)
for sp in compatible_species:
supercell = structure * scaling
nsp = supercell.composition[sp]
if sp.oxi_state == ox:
supercell.replace_species({sp: {sp: (nsp - 1) / nsp,
self.dopant: 1 / nsp}})
logger.info("Doping %s for %s at level %.3f" % (
sp, self.dopant, 1 / nsp))
elif self.codopant:
codopant = _find_codopant(sp, 2 * sp.oxi_state - ox)
supercell.replace_species({sp: {sp: (nsp - 2) / nsp,
self.dopant: 1 / nsp,
codopant: 1 / nsp}})
logger.info("Doping %s for %s + %s at level %.3f" % (
sp, self.dopant, codopant, 1 / nsp))
elif abs(sp.oxi_state) < abs(ox):
# Strategy: replace the target species with a
# combination of dopant and vacancy.
# We will choose the lowest oxidation state species as a
# vacancy compensation species as it is likely to be lower in
# energy
sp_to_remove = min([s for s in comp if s.oxi_state * ox > 0],
key=lambda ss: abs(ss.oxi_state))
if sp_to_remove == sp:
common_charge = lcm(int(abs(sp.oxi_state)), int(abs(ox)))
ndopant = common_charge / abs(ox)
nsp_to_remove = common_charge / abs(sp.oxi_state)
logger.info("Doping %d %s with %d %s." %
(nsp_to_remove, sp, ndopant, self.dopant))
supercell.replace_species(
{sp: {sp: (nsp - nsp_to_remove) / nsp,
self.dopant: ndopant / nsp}})
else:
ox_diff = int(abs(round(sp.oxi_state - ox)))
vac_ox = int(abs(sp_to_remove.oxi_state))
common_charge = lcm(vac_ox, ox_diff)
ndopant = common_charge / ox_diff
nx_to_remove = common_charge / vac_ox
nx = supercell.composition[sp_to_remove]
logger.info("Doping %d %s with %s and removing %d %s." %
(ndopant, sp, self.dopant,
nx_to_remove, sp_to_remove))
supercell.replace_species(
{sp: {sp: (nsp - ndopant) / nsp,
self.dopant: ndopant / nsp},
sp_to_remove: {
sp_to_remove: (nx - nx_to_remove) / nx}})
elif abs(sp.oxi_state) > abs(ox):
# Strategy: replace the target species with dopant and also
# remove some opposite charged species for charge neutrality
if ox > 0:
sp_to_remove = max(supercell.composition.keys(),
key=lambda el: el.X)
else:
sp_to_remove = min(supercell.composition.keys(),
key=lambda el: el.X)
# Confirm species are of opposite oxidation states.
assert sp_to_remove.oxi_state * sp.oxi_state < 0
ox_diff = int(abs(round(sp.oxi_state - ox)))
anion_ox = int(abs(sp_to_remove.oxi_state))
nx = supercell.composition[sp_to_remove]
common_charge = lcm(anion_ox, ox_diff)
ndopant = common_charge / ox_diff
nx_to_remove = common_charge / anion_ox
logger.info("Doping %d %s with %s and removing %d %s." %
(ndopant, sp, self.dopant,
nx_to_remove, sp_to_remove))
supercell.replace_species(
{sp: {sp: (nsp - ndopant) / nsp,
self.dopant: ndopant / nsp},
sp_to_remove: {sp_to_remove: (nx - nx_to_remove) / nx}})
ss = t.apply_transformation(
supercell, return_ranked_list=self.max_structures_per_enum)
logger.info("%s distinct structures" % len(ss))
all_structures.extend(ss)
logger.info("Total %s doped structures" % len(all_structures))
if return_ranked_list:
return all_structures[:return_ranked_list]
return all_structures[0]["structure"]
@property
def inverse(self):
return None
@property
def is_one_to_many(self):
return True
[docs]class SlabTransformation(AbstractTransformation):
"""
A transformation that creates a slab from a structure.
"""
def __init__(self, miller_index, min_slab_size, min_vacuum_size,
lll_reduce=False, center_slab=False,
in_unit_planes=False, primitive=True,
max_normal_search=None, shift=0, tol=0.1):
"""
Args:
miller_index (3-tuple or list): miller index of slab
min_slab_size (float): minimum slab size in angstroms
min_vacuum_size (float): minimum size of vacuum
lll_reduce (bool): whether to apply LLL reduction
center_slab (bool): whether to center the slab
primitive (bool): whether to reduce slabs to most primitive cell
max_normal_search (int): maximum index to include in linear
combinations of indices to find c lattice vector orthogonal
to slab surface
shift (float): shift to get termination
tol (float): tolerance for primitive cell finding
"""
self.miller_index = miller_index
self.min_slab_size = min_slab_size
self.min_vacuum_size = min_vacuum_size
self.lll_reduce = lll_reduce
self.center_slab = center_slab
self.in_unit_planes = in_unit_planes
self.primitive = primitive
self.max_normal_search = max_normal_search
self.shift = shift
self.tol = 0.1
[docs] def apply_transformation(self, structure):
sg = SlabGenerator(structure, self.miller_index, self.min_slab_size,
self.min_vacuum_size, self.lll_reduce,
self.center_slab, self.in_unit_planes,
self.primitive, self.max_normal_search)
slab = sg.get_slab(self.shift, self.tol)
return slab
@property
def inverse(self):
return None
@property
def is_one_to_many(self):
return None
[docs]class GrainBoundaryTransformation(AbstractTransformation):
"""
A transformation that creates a gb from a bulk structure.
"""
def __init__(self, rotation_axis, rotation_angle, expand_times=4, vacuum_thickness=0.0,
ab_shift=[0, 0], normal=False, ratio=None, plane=None, max_search=50,
tol_coi=1.e-3):
"""
Args:
rotation_axis (list): Rotation axis of GB in the form of a list of integer
e.g.: [1, 1, 0]
rotation_angle (float, in unit of degree): rotation angle used to generate GB.
Make sure the angle is accurate enough. You can use the enum* functions
in this class to extract the accurate angle.
e.g.: The rotation angle of sigma 3 twist GB with the rotation axis
[1, 1, 1] and GB plane (1, 1, 1) can be 60.000000000 degree.
If you do not know the rotation angle, but know the sigma value, we have
provide the function get_rotation_angle_from_sigma which is able to return
all the rotation angles of sigma value you provided.
expand_times (int): The multiple times used to expand one unit grain to larger grain.
This is used to tune the grain length of GB to warrant that the two GBs in one
cell do not interact with each other. Default set to 4.
vacuum_thickness (float): The thickness of vacuum that you want to insert between
two grains of the GB. Default to 0.
ab_shift (list of float, in unit of a, b vectors of Gb): in plane shift of two grains
normal (logic):
determine if need to require the c axis of top grain (first transformation matrix)
perperdicular to the surface or not.
default to false.
ratio (list of integers):
lattice axial ratio.
For cubic system, ratio is not needed.
For tetragonal system, ratio = [mu, mv], list of two integers,
that is, mu/mv = c2/a2. If it is irrational, set it to none.
For orthorhombic system, ratio = [mu, lam, mv], list of three integers,
that is, mu:lam:mv = c2:b2:a2. If irrational for one axis, set it to None.
e.g. mu:lam:mv = c2,None,a2, means b2 is irrational.
For rhombohedral system, ratio = [mu, mv], list of two integers,
that is, mu/mv is the ratio of (1+2*cos(alpha))/cos(alpha).
If irrational, set it to None.
For hexagonal system, ratio = [mu, mv], list of two integers,
that is, mu/mv = c2/a2. If it is irrational, set it to none.
plane (list): Grain boundary plane in the form of a list of integers
e.g.: [1, 2, 3]. If none, we set it as twist GB. The plane will be perpendicular
to the rotation axis.
max_search (int): max search for the GB lattice vectors that give the smallest GB
lattice. If normal is true, also max search the GB c vector that perpendicular
to the plane. For complex GB, if you want to speed up, you can reduce this value.
But too small of this value may lead to error.
tol_coi (float): tolerance to find the coincidence sites. When making approximations to
the ratio needed to generate the GB, you probably need to increase this tolerance to
obtain the correct number of coincidence sites. To check the number of coincidence
sites are correct or not, you can compare the generated Gb object's sigma with enum*
sigma values (what user expected by input).
Returns:
Grain boundary structure (Gb (Structure) object).
"""
self.rotation_axis = rotation_axis
self.rotation_angle = rotation_angle
self.expand_times = expand_times
self.vacuum_thickness = vacuum_thickness
self.ab_shift = ab_shift
self.normal = normal
self.ratio = ratio
self.plane = plane
self.max_search = max_search
self.tol_coi = tol_coi
[docs] def apply_transformation(self, structure):
gbg = GBGenerator(structure)
gb_struct = gbg.gb_from_parameters(
self.rotation_axis,
self.rotation_angle,
self.expand_times,
self.vacuum_thickness,
self.ab_shift,
self.normal,
self.ratio,
self.plane,
self.max_search,
self.tol_coi)
return gb_struct
@property
def inverse(self):
return None
@property
def is_one_to_many(self):
return None