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
    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 \
from pymatgen.analysis.structure_matcher import StructureMatcher, \
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__ = ""
__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 (int): The minimum cell size wanted. Must be an int. Defaults to 1. max_cell_size (int): The maximum cell size wanted. Must be an int. Defaults to 1. symm_prec (float): Tolerance to use for symmetry detection. Defaults to 0.1. occu_tol (int): If set, the code will first round and scale occupancies to the nearest rational number, with maximum denominator = occu_tol. This handles structures that contain partial occupancies that are close to a rational number. E.g., sometimes the reported occupancy is 0.249, and if occu_tol is set to 4, this will be rounded to 0.25. refine_structure (bool): 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, refinement 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, occu_tol=None, 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.occu_tol = occu_tol 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.occu_tol: species = [dict(d) for d in structure.species_and_occu] # Here, we rescale all occupancies such that they meet the frac # limit. for sp in species: for k, v in sp.items(): sp[k] = float(Fraction(v).limit_denominator(self.occu_tol)) structure = Structure(structure.lattice, species, structure.frac_coords) 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) 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 =, 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"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]]"Compatible species: %s" % compatible_species) lengths = scaling = [max(1, int(round(math.ceil(self.min_length/x)))) for x in lengths]"Lengths are %s" % str(lengths))"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}})"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}})"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)"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]"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"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)"%s distinct structures" % len(ss)) all_structures.extend(ss)"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