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
from warnings import warn
import logging
import math
import six
import warnings
from monty.fractions import lcm
from pymatgen.core.structure import Composition
from pymatgen.core.periodic_table import Element, Specie, get_el_sp
from pymatgen.transformations.transformation_abc import AbstractTransformation
from pymatgen.transformations.standard_transformations import \
SubstitutionTransformation, OrderDisorderedStructureTransformation
from pymatgen.command_line.enumlib_caller import EnumlibAdaptor
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
"""
This module implements more advanced transformations.
"""
__author__ = "Shyue Ping Ong, Stephen Dacek, Anubhav Jain"
__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.
"""
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):
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
[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]
)
if structure.is_ordered:
warn("Enumeration skipped for structure with composition {} "
"because it is ordered".format(structure.composition))
structures = [structure.copy()]
else:
adaptor = EnumlibAdaptor(
structure, min_cell_size=self.min_cell_size,
max_cell_size=self.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)
adaptor.run()
structures = adaptor.structures
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:
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 \
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, **kwargs):
self.kwargs = kwargs
self.threshold = threshold
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 MagOrderingTransformation(AbstractTransformation):
"""
This transformation takes a structure and returns a list of magnetic
orderings. Currently only works for ordered structures.
Args:
mag_elements_spin:
A mapping of elements/species to magnetically order to spin
magnitudes. E.g., {"Fe3+": 5, "Mn3+": 4}
order_parameter:
degree of magnetization. 0.5 corresponds to
antiferromagnetic order
energy_model:
Energy model used to rank the structures. Some models are
provided in :mod:`pymatgen.analysis.energy_models`.
**kwargs:
Same keyword args as :class:`EnumerateStructureTransformation`,
i.e., min_cell_size, etc.
"""
def __init__(self, mag_species_spin, order_parameter=0.5,
energy_model=SymmetryModel(), **kwargs):
self.mag_species_spin = mag_species_spin
if order_parameter > 1 or order_parameter < 0:
raise ValueError('Order Parameter must lie between 0 and 1')
else:
self.order_parameter = order_parameter
self.energy_model = energy_model
self.kwargs = kwargs
[docs] @classmethod
def determine_min_cell(cls, structure, mag_species_spin, order_parameter):
"""
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)
denom = Fraction(order_parameter).limit_denominator(100).denominator
atom_per_specie = [structure.composition[m]
for m in mag_species_spin.keys()]
n_gcd = six.moves.reduce(gcd, atom_per_specie)
if not n_gcd:
raise ValueError(
'The specified species do not exist in the structure'
' to be enumerated')
return lcm(int(n_gcd), denom) / n_gcd
[docs] def apply_transformation(self, structure, return_ranked_list=False):
# Make a mutable structure first
mods = Structure.from_sites(structure)
for sp, spin in self.mag_species_spin.items():
sp = get_el_sp(sp)
oxi_state = getattr(sp, "oxi_state", 0)
if spin:
up = Specie(sp.symbol, oxi_state, {"spin": abs(spin)})
down = Specie(sp.symbol, oxi_state, {"spin": -abs(spin)})
mods.replace_species(
{sp: Composition({up: self.order_parameter,
down: 1 - self.order_parameter})})
else:
mods.replace_species(
{sp: Specie(sp.symbol, oxi_state, {"spin": spin})})
if mods.is_ordered:
return [mods] if return_ranked_list > 1 else mods
enum_args = self.kwargs
enum_args["min_cell_size"] = max(int(
MagOrderingTransformation.determine_min_cell(
structure, self.mag_species_spin,
self.order_parameter)),
enum_args.get("min_cell_size", 1))
max_cell = enum_args.get('max_cell_size')
if max_cell:
if enum_args["min_cell_size"] > max_cell:
raise ValueError('Specified max cell size is smaller'
' than the minimum enumerable cell size')
else:
enum_args["max_cell_size"] = enum_args["min_cell_size"]
t = EnumerateStructureTransformation(**enum_args)
alls = t.apply_transformation(
mods, return_ranked_list=return_ranked_list)
try:
num_to_return = int(return_ranked_list)
except ValueError:
warnings.warn("return_ranked_list cannot be cast to an int. "
"Only 1 structure will be returned.")
num_to_return = 1
if num_to_return == 1 or not return_ranked_list:
return alls[0]["structure"] if num_to_return else alls
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, 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.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.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