def main():
s1name = "geometry/BCC_to_FCC/POSCAR_BCC"
s2name = "geometry/BCC_to_FCC/POSCAR_FCC"
s1 = Poscar.from_file(s1name).structure
s2 = Poscar.from_file(s2name).structure
# print s1,s2
smatcher = StructureMatcher(primitive_cell=False)
res = smatcher.get_transformation(s1, s2)
print res
def match_structures(s1, s2, scale_lattice=False, rh_only=True):
"""
Args
s1: high sym structure
s2: low sym structure
scale_lattice (optional): high_sym_superlcell has same lattice vectors as s2 (no strain)
Returns
basis: should be the basis that when applied to s1 makes a supercell of the size and orentation of s2
origin: any additional translation to best match (applied before applying the basis change to match what isotropy does)
displacements
high_sym_supercell
"""
sm = StructureMatcher(ltol=0.3,
stol=0.3,
angle_tol=15,
scale=True,
attempt_supercell=True,
primitive_cell=False)
basis, origin, mapping = sm.get_transformation(s1, s2, rh_only=rh_only)
struct_hs_supercell = sm.get_s2_like_s1(s1, s2, rh_only=rh_only)
# change origin from the supercell basis to the high sym basis
origin = np.round_(frac_vec_convert(origin, struct_hs_supercell.lattice,
s2.lattice),
decimals=5)
if scale_lattice:
hs_lat = struct_hs_supercell.lattice
hs_std = pmg.Lattice.from_lengths_and_angles(hs_lat.abc, hs_lat.angles)
ls_lat = s2.lattice
ls_std = pmg.Lattice.from_lengths_and_angles(ls_lat.abc, ls_lat.angles)
for aligned, rot, scale in hs_lat.find_all_mappings(hs_std):
if (abs(aligned.matrix - hs_lat.matrix) < 1.e-3).all():
strained_hs_lattice = pmg.Lattice(np.inner(ls_std.matrix, rot))
struct_hs_supercell = pmg.Structure(
strained_hs_lattice, struct_hs_supercell.species,
[site.frac_coords for site in struct_hs_supercell])
displacements = []
for s_hs, s_ls in zip(struct_hs_supercell, s1):
disp = np.round_(smallest_disp(s_hs.frac_coords, s_ls.frac_coords),
decimals=5)
displacements.append(disp)
return basis, origin, displacements, struct_hs_supercell
def find_common_transformation(asecell,
layer_indices,
ltol=0.05,
stol=0.05,
angle_tol=2.0): # pylint: disable=too-many-arguments,too-many-locals,too-many-branches,too-many-statements
"""
Given an input structure, in ASE format, and the list with
the indices of atoms belonging to each layer, determine
if there exists a common transformation that brings one
layer to the next.
:note: it MUST receive the layers already rearranged so that they are ordered
along the stacking axis, and with all atoms nearby (i.e., when removing the PBC
along the third axis, I should still see the layer and not it broken in two or more parts).
This is performed by the function `find_layers`.
:param asecell: ASE structure of the bulk, where the first two
lattice vectors have been re-oriented to lie
in the plane of the layers
:param layer_indices: list of lists containing the indices of the
atoms belonging to each layer
:param ltol: tolerance on cell length
:param stol: tolerance on atomic site positions
:param angle_tol: tolerance on cell angles
:return: a tuple of length three: either (rot, transl, None) if there is a common transformation,
or (None, None, message) if a common transformation could not be found.
IMPORTANT: the code will try to find a transformation matrix so that rot[2,2] > 0, if it exists.
If it does not find it, and it returns rot[2,2] < 0, it means it's a category III material (see
definition in the text). Otherwise, it's either category I or II depending on the monolayer symmetry).
"""
# instance of the adaptor to convert ASE structures to pymatgen format
adaptor = AseAtomsAdaptor()
# Create an instance of Structure Matcher with the given tolerances
sm = StructureMatcher(ltol, stol, angle_tol, primitive_cell=False)
# Number of layers
num_layers = len(layer_indices)
# If there is only one layer, the transformation is
# simply a translation along the third axis
if num_layers == 1:
return np.eye(3), asecell.cell[2], np.zeros(3), None
# If we are here, there are at least two layers
# First check that all layers are identical
layers = [asecell[layer] for layer in layer_indices]
if not layers_match(layers, ltol=ltol, stol=stol, angle_tol=angle_tol):
return None, None, None, "Layers are not identical"
# Layers are already ordered according to their
# projection along the stacking direction
# we start by comparing the first and second layer
# As we did in function `layers_match`, we put the third vector orthogonal to the plane and
# extend the third direction slightly
layer0 = layers[0].copy()
layer0.cell[2] = [0, 0, 2 * asecell.cell[2, 2]]
layer1 = layers[1].copy()
layer1.cell[2] = [0, 0, 2 * asecell.cell[2, 2]]
str0 = adaptor.get_structure(layer0)
str1 = adaptor.get_structure(layer1)
# This is the transformation that brings layer0 onto layer1
# it is a tuple with a supercell matrix, a translation vector, and the indices
# of how atoms are rearranged
transformation01 = sm.get_transformation(str1, str0)
# There are still cases that, even if layers_match was OK, here we get `None` for transformation.
# Probably the difference with the code above is in the thresholds and/or in the way the pymatgen
# code internally implements it. Note: probably we could skip the call to `layers_match` above
# and just keep the logic below!
# To avoid crashes, we cope with this here
if transformation01 is None:
return None, None, None, "No transformation to match first and second layer"
# define the common lattice of both layers (which is the one of the bulk)
# we use twice the same "common lattice" because they are identical in our case
# in general we should have str0.lattice and str1.lattice (not necessarily in this order)
common_lattice = str0.lattice
# find the expression of the rotation in the common lattice
rot01 = np.linalg.solve(np.dot(transformation01[0], common_lattice.matrix),
common_lattice.matrix)
# translation vector in Cartesian coordinates
tr01 = np.matmul(common_lattice.matrix.T, transformation01[1])
# this is the "symmetry" operation that brings layer0 into layer1
op01 = SymmOp.from_rotation_and_translation(rot01, tr01)
# While on the x-y plane we are OK if we find a periodic image, on z we want to
# drop periodic boundary conditions, so finding a periodic image (using the periodicity
# of the bulk) is not OK.
# We therefore check if we can identify the same translation along z for all atoms
# Note that I only take the z coordinate (last column); this is in angstrom
required_z_shift_per_atom = (
layers[1].positions -
op01.operate_multi(layers[0].positions)[transformation01[2]])[:, 2]
# Note however that if the layers are not properly defined with all atoms closeby,
# The values of this array might not be all identical.
# E.g. if atoms of the same layer are not closeby unless one considers PBC.
z_shift = required_z_shift_per_atom[0]
for atom_idx in range(1, len(required_z_shift_per_atom)):
# NOTE: This test is going to work because the function called before this
# already reorganised layers to have all atoms close to each other
assert np.abs(z_shift - required_z_shift_per_atom[atom_idx]) < SYMPREC
# Get the spacegroup of the monolayer (useful below)
spg_mono = SpacegroupAnalyzer(adaptor.get_structure(layer0),
symprec=SYMPREC)
# If the transformation involves a flip in the z-direction
# we check if, by combining it with a symmetry of the monolayer,
# we get a transformation that does NOT flip z
# As soon as I find one, I replace tr01, rot01 and op01 (the combination of tr01 and rot01)
# with those we found, so that the axis is not flipped and transformation01[0][2, 2] > 0
if transformation01[0][2, 2] < 0:
for op in spg_mono.get_symmetry_operations(cartesian=True):
affine_prod = np.dot(op01.affine_matrix, op.affine_matrix)
if affine_prod[2, 2] > 0:
tr01 = affine_prod[0:3][:, 3]
rot01 = affine_prod[0:3][:, 0:3]
op01 = SymmOp.from_rotation_and_translation(rot01, tr01)
break
# Here, there are now two options:
# - I couldn't find any operation: then I still have transformation01[0][2, 2] < 0
# and I will categorize it as category III
# - I could find it, then transformation01[0][2, 2] > 0 and it's either category I or II
# (depending on the monolayer symmetry)
# It could also be category III in weird cases like identical layers that are invariant
# under z -> -z but with two alternating interlayer distances, i.e. "dimerized"
# I now use op01 to check if, with op01, I can bring each layer onto the next,
# and layer num_layers onto num_layers+1, i.e. the first one + the third lattice vector
# If op01 does not work we might need to combine it with symmetry operations of the monolayer
# before concluding that the system is not a MDO polytype
# print(op01)
for symop in sorted(
spg_mono.get_symmetry_operations(cartesian=True),
key=lambda op: -op.affine_matrix[2, 2],
):
# We sort the symmetry operations of the monolayer so that the ones that
# do NOT flip z are considered first.
# Indeed in principles operations that flip z should be possible
# only in category I, but would result in a coincidence operation
# that flip z, which is not necessary in this case (category I), as in this case
# we'll find another one that does the same job without flipping, so we could even skip these operations.
# (also because we want to give the guarantee that, if symop.affine_matrix[2, 2] < 0, then we are
# in category III)
# Still, operations that flip z could be relevant in some weird cases of category III, called "dimerized" above
# So that in the end we will find a coincidence operation that flips z even if op01 did not
#
# Combine the coincidence operation with the symmetry operation of the monolayer
affine_prod = np.dot(op01.affine_matrix, symop.affine_matrix)
this_rot = affine_prod[0:3][:, 0:3]
this_tr = affine_prod[0:3][:, 3]
# The coincidence operation, elevated to the number of layers,
# should be a symmetry operation of the monolayer, and we need the
# corresponding fractional translation
frac_tr = get_fractional_translation(this_rot, num_layers, spg_mono)
if frac_tr is None:
found_common = False
continue
# Check if the coincidence operation satisfies a complex condition, implemented
# in 'check_transformation'. If not, there is no need to continue
# with this transformation
if not check_transformation(this_rot, this_tr, frac_tr, asecell,
num_layers):
found_common = False
continue
# If the condition is instead satisfied, we can continue and decompose the translation
# vector this_tr, into a component which is just associated with the fact that
# the coincidence operation has to be performed around a point this_tr0 different from the origin
# and true traslation, which is either purely vertical or has a component invariant
# under the coincidence transormation (e.g. a component parallel to a mirror plane)
this_tr0 = get_transformation_center(this_rot, this_tr, asecell,
num_layers)
# Once we have identified possible component associated with a non-trivial center of the
# coincidence operation, this_tr0, we can subtract it from this_tr to obtain the true translation
this_tr -= np.dot(np.identity(3) - this_rot, this_tr0)
# The coincidence operation is then defined in terms of this_tr only
this_op = SymmOp.from_rotation_and_translation(this_rot, this_tr)
# Bring back the inplane components of the vector this_tr0 identifying the center of
# the coincidence operation inside the unit cell
this_tr0[:2] = np.dot(
np.dot(this_tr0, np.linalg.inv(asecell.cell))[:2] % 1,
asecell.cell[:2])[:2]
# It is also useful to define a vector along the stacking direction
vec = np.array([0.0, 0.0, asecell.cell[2, 2] / num_layers])
found_common = True
# NOTE: here we start from layer ZERO! The reason is that we want to
# double check that now that we made a combination with a symmetry operation,
# we still send layer 1 into layer 2.
# So we want to check that case as well.
for il in range(0, num_layers):
# We need to copy the layers as we'll change them in place
layer0 = layers[il].copy()
layer1 = layers[(il + 1) % num_layers].copy()
# translate back the two layers to put at the origin the
# center around which we want to perform the coincidence operation
# if layer1 is the layer num_layer + 1 we need to translate it
# by a full lattice vector
layer0.translate(-il * vec - this_tr0)
layer1.translate((-il * vec - this_tr0 + np.floor(
(il + 1.0) / num_layers) * asecell.cell[2]))
# the transformed positions of the atoms in the first layer
pos0 = this_op.operate_multi(layer0.positions)
# that should be identical to the ones of the second layer
pos1 = layer1.positions
# we already know from above that the species in each layer are identical
# so we take the set of the ones in the first layer
for an in set(layer0.get_chemical_symbols()):
# check, species by species, that the atomic positions are identical
# up to a threshold
# The second variable would be the mapping, but I'm not using it
distance, _ = are_same_except_order(
np.array([
_[0] for _ in zip(pos0, layer0.get_chemical_symbols())
if _[1] == an
]),
np.array([
_[0] for _ in zip(pos1, layer1.get_chemical_symbols())
if _[1] == an
]),
common_lattice.matrix,
)
# if the distance for any of the atoms of the given species
# is larger than the threshold the transformation is not the same
# between all consecutive layers
found_common = found_common and distance.max() < stol
# if this transformation does not work it is useless con continue with
# other species of this layer
if not found_common:
break
# if this transformation does not work it is useless con continue with
# subsequent layers
if not found_common:
break
# if the transformation works, no need to test additional transformations
if found_common:
break
if not found_common:
return (
None,
None,
None,
"The transformation between consecutive layers is not always the same",
)
return this_rot, this_tr, this_tr0, None