from collections import abc
import numpy as np
from stk._internal.building_block import BuildingBlock
from stk._internal.optimizers.null import NullOptimizer
from stk._internal.optimizers.optimizer import Optimizer
from stk._internal.reaction_factories.generic_reaction_factory import (
GenericReactionFactory,
)
from stk._internal.reaction_factories.reaction_factory import ReactionFactory
from stk._internal.topology_graphs.edge import Edge
from .cof import Cof
from .vertices import LinearVertex, NonLinearVertex
[docs]
class PeriodicKagome(Cof):
"""
Represents a periodic kagome COF topology graph.
Unoptimized construction
.. moldoc::
import moldoc.molecule as molecule
import stk
cof = stk.ConstructedMolecule(
topology_graph=stk.cof.PeriodicKagome(
building_blocks=(
stk.BuildingBlock(
smiles='BrCC(Br)',
functional_groups=[stk.BromoFactory()],
),
stk.BuildingBlock(
smiles='BrC1C(Br)CC(Br)C(Br)C1',
functional_groups=[stk.BromoFactory()],
),
),
lattice_size=(3, 3, 1),
),
)
moldoc_display_molecule = molecule.Molecule(
atoms=(
molecule.Atom(
atomic_number=atom.get_atomic_number(),
position=position,
) for atom, position in zip(
cof.get_atoms(),
cof.get_position_matrix(),
)
),
bonds=(
molecule.Bond(
atom1_id=bond.get_atom1().get_id(),
atom2_id=bond.get_atom2().get_id(),
order=(
1
if bond.get_order() == 9
else bond.get_order()
),
) for bond in cof.get_bonds()
if all(p == 0 for p in bond.get_periodicity())
),
)
``Collapser(scale_steps=False)`` optimized construction
.. moldoc::
import moldoc.molecule as molecule
import stk
cof = stk.ConstructedMolecule(
topology_graph=stk.cof.PeriodicKagome(
building_blocks=(
stk.BuildingBlock(
smiles='BrCC(Br)',
functional_groups=[stk.BromoFactory()],
),
stk.BuildingBlock(
smiles='BrC1C(Br)CC(Br)C(Br)C1',
functional_groups=[stk.BromoFactory()],
),
),
lattice_size=(3, 3, 1),
optimizer=stk.Collapser(scale_steps=False),
),
)
moldoc_display_molecule = molecule.Molecule(
atoms=(
molecule.Atom(
atomic_number=atom.get_atomic_number(),
position=position,
) for atom, position in zip(
cof.get_atoms(),
cof.get_position_matrix(),
)
),
bonds=(
molecule.Bond(
atom1_id=bond.get_atom1().get_id(),
atom2_id=bond.get_atom2().get_id(),
order=(
1
if bond.get_order() == 9
else bond.get_order()
),
) for bond in cof.get_bonds()
if all(p == 0 for p in bond.get_periodicity())
),
)
Building blocks with four and two functional groups are required
for this topology graph.
When using a :class:`dict` for the `building_blocks` parameter,
as in :ref:`cof-topology-graph-examples`:
*Multi-Building Block COF Construction*, a
:class:`.BuildingBlock`, with the following number of functional
groups, needs to be assigned to each of the following vertex ids:
| 4-functional groups: 0 to 2
| 2-functional groups: 3 to 8
Note that optimizers may not optimize the :class:`.PeriodicInfo`.
The documentation of the optimizer will state if it does.
See :class:`.Cof` for more details and examples.
"""
def __init__(
self,
building_blocks: (
abc.Iterable[BuildingBlock] | dict[BuildingBlock, tuple[int, ...]]
),
lattice_size: tuple[int, int, int],
vertex_alignments: dict[int, int] | None = None,
reaction_factory: ReactionFactory = GenericReactionFactory(),
num_processes: int = 1,
optimizer: Optimizer = NullOptimizer(),
scale_multiplier: float = 1.0,
) -> None:
"""
Initialize a :class:`.PeriodicKagome` instance.
Parameters:
building_blocks:
Can be a :class:`tuple` of :class:`.BuildingBlock`
instances, which should be placed on the topology graph.
Can also be a :class:`dict` which maps the
:class:`.BuildingBlock` instances to the ids of the
vertices it should be placed on. A :class:`dict` is
required when there are multiple building blocks with the
same number of functional groups, because in this case
the desired placement is ambiguous.
lattice_size:
The size of the lattice in the x, y and z directions.
vertex_alignments:
A mapping from the id of a :class:`.Vertex`
to an :class:`.Edge` connected to it.
The :class:`.Edge` is used to align the first
:class:`.FunctionalGroup` of a :class:`.BuildingBlock`
placed on that vertex. Only vertices which need to have
their default edge changed need to be present in the
:class:`dict`. If ``None`` then the default edge is used
for each vertex. Changing which :class:`.Edge` is used will
mean that the topology graph represents different
structural isomers. The edge is referred to by a number
between ``0`` (inclusive) and the number of edges the
vertex is connected to (exclusive).
reaction_factory:
The reaction factory to use for creating bonds between
building blocks.
num_processes:
The number of parallel processes to create during
:meth:`construct`.
optimizer:
Used to optimize the structure of the constructed
molecule.
scale_multiplier:
Scales the positions of the vertices.
Raises:
:class:`AssertionError`
If the any building block does not have a
valid number of functional groups.
:class:`ValueError`
If the there are multiple building blocks with the
same number of functional_groups in `building_blocks`,
and they are not explicitly assigned to vertices. The
desired placement of building blocks is ambiguous in
this case.
:class:`~.cof.UnoccupiedVertexError`
If a vertex of the COF topology graph does not have a
building block placed on it.
:class:`~.cof.OverlyOccupiedVertexError`
If a vertex of the COF topology graph has more than one
building block placed on it.
"""
super().__init__(
building_blocks=building_blocks,
lattice_size=lattice_size,
periodic=True,
vertex_alignments=vertex_alignments,
reaction_factory=reaction_factory,
num_processes=num_processes,
optimizer=optimizer,
scale_multiplier=scale_multiplier,
)
_lattice_constants = _a, _b, _c = (
np.array([1.0, 0.0, 0.0]),
np.array([0.5, 0.866, 0.0]),
np.array([0.0, 0.0, 5 / 1.7321]),
)
_non_linears = (
NonLinearVertex(0, (1 / 4) * _a + (3 / 4) * _b + (0.5) * _c),
NonLinearVertex(1, (3 / 4) * _a + (3 / 4) * _b + (1 / 2) * _c),
NonLinearVertex(2, (3 / 4) * _a + (1 / 4) * _b + (1 / 2) * _c),
)
_vertex_prototypes = (
*_non_linears,
LinearVertex.init_at_center(
id=3,
vertices=(_non_linears[0], _non_linears[1]),
),
LinearVertex.init_at_center(
id=4,
vertices=(_non_linears[0], _non_linears[2]),
),
LinearVertex.init_at_center(
id=5,
vertices=(_non_linears[1], _non_linears[2]),
),
LinearVertex.init_at_shifted_center(
id=6,
vertices=(_non_linears[0], _non_linears[1]),
cell_shifts=((0, 0, 0), (-1, 0, 0)),
lattice_constants=_lattice_constants,
),
LinearVertex.init_at_shifted_center(
id=7,
vertices=(_non_linears[0], _non_linears[2]),
cell_shifts=((0, 0, 0), (-1, 1, 0)),
lattice_constants=_lattice_constants,
),
LinearVertex.init_at_shifted_center(
id=8,
vertices=(_non_linears[1], _non_linears[2]),
cell_shifts=((0, 0, 0), (0, 1, 0)),
lattice_constants=_lattice_constants,
),
)
_edge_prototypes = (
Edge(0, _vertex_prototypes[3], _vertex_prototypes[0]),
Edge(1, _vertex_prototypes[3], _vertex_prototypes[1]),
Edge(2, _vertex_prototypes[4], _vertex_prototypes[0]),
Edge(3, _vertex_prototypes[4], _vertex_prototypes[2]),
Edge(4, _vertex_prototypes[5], _vertex_prototypes[1]),
Edge(5, _vertex_prototypes[5], _vertex_prototypes[2]),
Edge(6, _vertex_prototypes[6], _vertex_prototypes[0]),
Edge(
id=7,
vertex1=_vertex_prototypes[6],
vertex2=_vertex_prototypes[1],
periodicity=(-1, 0, 0),
),
Edge(8, _vertex_prototypes[7], _vertex_prototypes[0]),
Edge(
id=9,
vertex1=_vertex_prototypes[7],
vertex2=_vertex_prototypes[2],
periodicity=(-1, 1, 0),
),
Edge(10, _vertex_prototypes[8], _vertex_prototypes[1]),
Edge(
id=11,
vertex1=_vertex_prototypes[8],
vertex2=_vertex_prototypes[2],
periodicity=(0, 1, 0),
),
)