# Source code for wulffpack.decahedron

```
from typing import Dict
import numpy as np
from ase import Atoms
from .core import BaseParticle
from .core.form import setup_forms
from .core.geometry import (get_standardized_structure,
get_symmetries,
get_angle,
get_rotation_matrix,
break_symmetry)
[docs]
class Decahedron(BaseParticle):
"""
A `Decahedron` object is a generalized Wulff construction
of a decahedral particle.
Parameters
----------
surface_energies
A dictionary with surface energies, where keys are
Miller indices and values surface energies (per area)
in a unit of choice, such as J/m^2.
twin_energy
Energy per area for twin boundaries
primitive_structure
Primitive cell to define the atomic structure used if an atomic
structure is requested. By default, an Au FCC structure is used.
The crystal has to have cubic symmetry.
natoms
Together with `lattice_parameter`, this parameter
defines the volume of the particle. If an atomic structure
is requested, the number of atoms will as closely as possible
match this value.
symprec
Numerical tolerance for symmetry analysis, forwarded to spglib.
tol
Numerical tolerance parameter.
Example
-------
The following example illustrates some possible uses of a
`Decahedron` object::
>>> from wulffpack import Decahedron
>>> from ase.build import bulk
>>> from ase.io import write
>>> surface_energies = {(1, 1, 1): 1.0, (1, 0, 0): 1.14}
>>> prim = bulk('Au')
>>> particle = Decahedron(surface_energies,
... twin_energy=0.03,
... primitive_structure=bulk('Au'))
>>> particle.view()
>>> write('decahedron.xyz', particle.atoms) # Writes atomic structure
"""
def __init__(self,
surface_energies: Dict[tuple, float],
twin_energy: float,
primitive_structure: Atoms = None,
natoms: int = 1000,
symprec: float = 1e-5,
tol: float = 1e-5):
standardized_structure = get_standardized_structure(primitive_structure, symprec)
symmetries = get_symmetries(standardized_structure, symprec)
if len(symmetries) < 48:
raise ValueError('A decahedron can only be created with a '
'primitive structure that has cubic symmetry')
twin_boundaries = [(-1, 1, 1), (1, -1, 1)]
symmetry_axes = [(0, 0, 1), np.cross(*twin_boundaries)]
inversion = [False, True]
broken_symmetries = break_symmetry(symmetries, symmetry_axes, inversion)
if twin_energy > 0.5 * min(surface_energies.values()):
raise ValueError('The construction expects a twin energy '
'that is smaller than 50 percent of the '
'smallest surface energy.')
surface_energies = surface_energies.copy()
surface_energies['twin'] = twin_energy
forms = setup_forms(surface_energies,
standardized_structure.cell.T,
broken_symmetries,
symmetries,
twin_boundary=twin_boundaries[0])
super().__init__(forms=forms,
standardized_structure=standardized_structure,
natoms=natoms,
ngrains=5,
volume_scale=_get_decahedral_scale_factor(),
tol=tol)
# Duplicate a single tetrahedron to form a complete decahedron
# with five tetrahedra
# Rotate such that fivefold axis is aligned with z axis
fivefold_vector = self.fivefold_axis_vector
rotation_axis = np.cross(fivefold_vector, [0, 0, 1])
angle = np.arccos(fivefold_vector[2] / np.linalg.norm(fivefold_vector))
R = get_rotation_matrix(angle, rotation_axis)
self.rotate_particle(R)
# Translate such that fivefold axis in x, y = 0, 0.
# Dangerous to use max and min in z because the particle
# may be truncated such that are several with the same value.
# Instead check which vertices are furthest away from the Ino face.
vertices = np.array(self._twin_form.facets[0].vertices)
# hard code because there may not be an Ino facet...
direction = np.array([1, 1, 0])
projections = [np.dot(v, direction) for v in vertices]
projections, ids = zip(*sorted(zip(projections,
list(range(len(projections))))))
min_vertex = vertices[ids[0]]
max_vertex = vertices[ids[1]]
# Check that rotation did its job
assert np.linalg.norm(max_vertex[:2] - min_vertex[:2]) < 1e-5
translation = np.array([-min_vertex[0], -min_vertex[1],
-(min_vertex[2] + max_vertex[2]) / 2])
self.translate_particle(translation)
# Rotate such that Ino facet aligns with a Cartesian vector
current = self._twin_form.facets[0].normal + self._twin_form.facets[1].normal
assert abs(current[2]) < 1e-5
target = (0, -1, 0)
angle = get_angle(current, target)
R = get_rotation_matrix(angle, [0, 0, 1])
self.rotate_particle(R)
# Back up the vertices in a separate list
# (that list is useful if creating an Atoms object)
for facet in self._yield_facets():
facet.original_vertices = [vertex.copy() for vertex in facet.vertices]
# Strain the particle to fill space
scale = _get_decahedral_scale_factor()
for facet in self._yield_facets():
for i in range(len(facet.vertices)):
facet.vertices[i][0] *= _get_decahedral_scale_factor()
# Scale normal too
facet.normal[1] *= scale
facet.normal[2] *= scale
facet.normal /= np.linalg.norm(facet.normal)
# Make five grains
rotations = []
for i in range(1, 5):
rotations.append(get_rotation_matrix(i * 2 * np.pi / 5, [0, 0, 1]))
self._duplicate_particle(rotations)
@property
def atoms(self) -> Atoms:
"""
Returns an ASE Atoms object
"""
atoms = self._get_atoms()
# We delete one atomic layer on a twin facet because we will
# get it back when we make five grains.
# But we need to save the atoms on the five-fold axis.
# Begin with identifying the maximum projection on one of the
# twin facets.
max_projection = -1e9
twin_direction = self._twin_form.facets[0].original_normal
for atom in atoms:
projection = np.dot(twin_direction, atom.position)
if projection > max_projection:
max_projection = projection
# Now delete all atoms that are
# close to the maximum projection
fivefold_atoms = Atoms()
twin_indices = []
for atom in atoms:
projection = np.dot(twin_direction, atom.position)
if abs(projection - max_projection) < 1e-5:
twin_indices.append(atom.index)
if abs(atom.position[0]) < 1e-5 and abs(atom.position[1]) < 1e-5:
fivefold_atoms.append(atom)
del atoms[twin_indices]
# Increase the distance from the x=0 plane by 1.8 % for each atom
for atom in atoms:
atom.position[0] = atom.position[0] * _get_decahedral_scale_factor()
# Make five grains
rotations = []
for i in range(1, 5):
rotations.append(get_rotation_matrix(i * 2 * np.pi / 5, [0, 0, 1]))
new_positions = []
for R in rotations:
for atom in atoms:
new_positions.append(np.dot(R, atom.position))
atoms += Atoms('{}{}'.format(self.standardized_structure[0].symbol,
len(new_positions)),
positions=new_positions)
atoms += fivefold_atoms
return atoms
@property
def fivefold_axis_vector(self) -> np.ndarray:
"""
Returns a vector pointing in the
direction of the five-fold axis.
"""
twins = self._twin_form.facets
v = np.cross(twins[0].normal,
twins[1].normal)
# Normalize
v /= np.linalg.norm(v)
return v
[docs]
def get_strain_energy(self, shear_modulus: float, poissons_ratio: float) -> float:
"""
Return strain energy as estimated with the formula provided in
A. Howie and L. D. Marks in Phil. Mag. A **49**, 95 (1984)
[HowMar84]_ (Eq. 10), which assumes an inhomogeneous strain in
the particle.
Warning
-------
This value is only approximate. If the decahedron is
heavily truncated, the returned strain energy may be highly
inaccurate.
Parameters
----------
shear_modulus
Shear modulus of the material
poissons_ratio
Poisson's ratio of the material
"""
eps_D = 0.0205
strain_energy_density = shear_modulus * eps_D ** 2 / 4
strain_energy_density /= (1 - poissons_ratio)
return strain_energy_density * self.volume
@property
def aspect_ratio(self) -> float:
"""
Returns the aspect ratio of the decahedron, here defined as
the ratio between the longest distance between two vertices
projected on the fivefold axis versus the longest distance
between two vertices projected on the plane perpendicular
to the fivefold axis.
"""
z_coords = []
xy_coords = []
for facet in self._yield_facets():
for vertex in facet.vertices:
z_coords.append(vertex[2])
xy_coords.append(vertex[:2])
# Longest distance between two points along z axis
# (because the particle has been rotated so that
# fivefold axis aligns with z)
height = max(z_coords) - min(z_coords)
# Find longest distance between two points in xy plane
width = 0
for i, xy_coord_i in enumerate(xy_coords):
for xy_coord_j in xy_coords[i + 1:]:
dist = ((xy_coord_i - xy_coord_j)**2).sum()
if dist > width:
width = dist
return height / width**0.5
def _get_decahedral_scale_factor() -> float:
return np.sqrt(10 - 4 * np.sqrt(5))
```