Covalent Organic Framework

class Cof(building_blocks, lattice_size, periodic=False, vertex_alignments=None, reaction_factory=GenericReactionFactory(), num_processes=1, optimizer=<stk.molecular.topology_graphs.topology_graph.optimizers.null.NullOptimizer object>)[source]

Bases: stk.molecular.topology_graphs.topology_graph.topology_graph.topology_graph.TopologyGraph

Represents a COF topology graph.

Notes

COF topologies are added by creating a subclass, which defines the _vertex_prototypes and _edge_prototypes class attributes.

Examples

Subclass Implementation

The source code of the subclasses, listed in cof, can serve as good examples.

Basic Construction

Cof instances can be made by providing the building block molecules and lattice size only (using Honeycomb as an example)

import stk

bb1 = stk.BuildingBlock('BrCCBr', [stk.BromoFactory()])
bb2 = stk.BuildingBlock('BrCC(CBr)CBr', [stk.BromoFactory()])
cof = stk.ConstructedMolecule(
    topology_graph=stk.cof.Honeycomb((bb1, bb2), (3, 3, 1)),
)

Suggested Optimization

For Cof topologies, it is recommend to use the Collapser optimizer if using the nonperiodic form. For periodic systems, the PeriodicCollapser is recommended.

import stk

bb1 = stk.BuildingBlock('BrCCBr', [stk.BromoFactory()])
bb2 = stk.BuildingBlock('BrCC(CBr)CBr', [stk.BromoFactory()])

# Nonperiodic.
cof = stk.ConstructedMolecule(
    topology_graph=stk.cof.Honeycomb(
        building_blocks=(bb1, bb2),
        lattice_size=(3, 3, 1),
        # Setting scale_steps to False tends to lead to a
        # better structure.
        optimizer=stk.Collapser(scale_steps=False),
    ),
)

# Periodic.
cof = stk.ConstructedMolecule(
    topology_graph=stk.cof.PeriodicHoneycomb(
        building_blocks=(bb1, bb2),
        lattice_size=(3, 3, 1),
        optimizer=stk.PeriodicCollapser(),
    ),
)

Accessing the Periodic Information

When building periodic Cof instances, the periodic information, such as the unit cell, can be accessed if you use the PeriodicConstructionResult returned by calling Cof.construct()

import stk

bb1 = stk.BuildingBlock('BrCCBr', [stk.BromoFactory()])
bb2 = stk.BuildingBlock('BrCC(CBr)CBr', [stk.BromoFactory()])

topology_graph = stk.cof.PeriodicHoneycomb(
    building_blocks=(bb1, bb2),
    lattice_size=(3, 3, 1),
)
construction_result = topology_graph.construct()
cof = stk.ConstructedMolecule.init_from_construction_result(
    construction_result=construction_result,
)
periodic_info = construction_result.get_periodic_info()
cell_matrix = periodic_info.get_cell_matrix()
# Can access all unit-cell parameters.
a = periodic_info.get_a()
b = periodic_info.get_b()
c = periodic_info.get_c()
alpha = periodic_info.get_alpha()
beta = periodic_info.get_beta()
gamma = periodic_info.get_gamma()
# Write to .pdb file.
writer = stk.PdbWriter()
writer.write(
    molecule=cof,
    path='cof.pdb',
    periodic_info=periodic_info,
)

Structural Isomer Construction

Different structural isomers of COFs can be made by using the vertex_alignments optional parameter

import stk

bb1 = stk.BuildingBlock('BrCCBr', [stk.BromoFactory()])
bb2 = stk.BuildingBlock('BrCC(CBr)CBr', [stk.BromoFactory()])

cof2 = stk.ConstructedMolecule(
    topology_graph=stk.cof.Honeycomb(
        building_blocks=(bb1, bb2),
        lattice_size=(3, 3, 1),
        vertex_alignments={0: 2, 1: 1, 2: 1},
    ),
)

The parameter maps the id of a vertex to a number between 0 (inclusive) and the number of edges the vertex is connected to (exclusive). So a vertex connected to three edges can be mapped to 0, 1 or 2.

By changing which edge each vertex is aligned with, a different structural isomer of the COF can be formed.

Multi-Building Block COF Construction

You can also build COFs with multiple building blocks, but, if you have multiple building blocks with the same number of functional groups, you have to assign each building block to the vertex you want to place it on

import stk

bb1 = stk.BuildingBlock('BrCCBr', [stk.BromoFactory()])
bb2 = stk.BuildingBlock('BrCNCBr', [stk.BromoFactory()])
bb3 = stk.BuildingBlock('BrCC(CBr)CBr', [stk.BromoFactory()])
bb4 = stk.BuildingBlock('BrCC(NCBr)CBr', [stk.BromoFactory()])

cof = stk.ConstructedMolecule(
    topology_graph=stk.cof.Honeycomb(
        # building_blocks is now a dict, which maps building
        # blocks to the id of the vertices it should be placed
        # on. You can use ranges to specify the ids.
        building_blocks={
            bb1: (2, 4, 7, 8, 9, 12, 13, 14, 17, 18, 19),
            bb2: 3,
            bb3: (0, 10, 11, 15, 16),
            bb4: (1, 5, 6),
        },
        lattice_size=(2, 2, 1),
    ),
)

You can combine this with the vertex_alignments parameter

cof2 = stk.ConstructedMolecule(
    topology_graph=stk.cof.Honeycomb(
        building_blocks={
            bb1: (2, 4, 7, 8, 9, 12, 13, 14, 17, 18, 19),
            bb2: 3,
            bb3: (0, 10, 11, 15, 16),
            bb4: (1, 5, 6),
        },
        lattice_size=(2, 2, 1),
        vertex_alignments={0: 2, 1: 1, 2: 1},
    ),
)

Methods

clone()

Return a clone.

construct()

Construct a ConstructedMolecule.

get_building_blocks()

Yield the building blocks.

get_num_building_block(building_block)

Get the number of times building_block is present.

with_building_blocks(building_block_map)

Return a clone holding different building blocks.

__init__(building_blocks, lattice_size, periodic=False, vertex_alignments=None, reaction_factory=GenericReactionFactory(), num_processes=1, optimizer=<stk.molecular.topology_graphs.topology_graph.optimizers.null.NullOptimizer object>)[source]

Initialize a Cof instance.

Parameters
  • building_blocks (tuple or dict) –

    Can be a tuple of BuildingBlock instances, which should be placed on the topology graph.

    Can also be a dict which maps the BuildingBlock instances to the ids of the vertices it should be placed on. A 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 (tuple of int) – The size of the lattice in the x, y and z directions.

  • periodic (bool, optional) – Toggle the construction of a periodic molecule. If True, periodic bonds will be made across the edges of the lattice.

  • vertex_alignments (dict, optional) – A mapping from the id of a Vertex to an Edge connected to it. The Edge is used to align the first FunctionalGroup of a BuildingBlock placed on that vertex. Only vertices which need to have their default edge changed need to be present in the dict. If None then the default edge is used for each vertex. Changing which 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 (ReactionFactory, optional) – The reaction factory to use for creating bonds between building blocks.

  • num_processes (int, optional) – The number of parallel processes to create during construct().

  • optimizer (Optimizer, optional) – Used to optimize the structure of the constructed molecule.

Raises
  • AssertionError – If the any building block does not have a valid number of functional groups.

  • 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.

  • UnoccupiedVertexError – If a vertex of the COF topology graph does not have a building block placed on it.

  • OverlyOccupiedVertexError – If a vertex of the COF topology graph has more than one building block placed on it.

clone()[source]

Return a clone.

Returns

The clone.

construct()[source]

Construct a ConstructedMolecule.

Returns

The data describing the ConstructedMolecule.

Return type

PeriodicConstructionResult

get_building_blocks()

Yield the building blocks.

Building blocks are yielded in an order based on their position in the topology graph. For two equivalent topology graphs, but with different building blocks, equivalently positioned building blocks will be yielded at the same time.

Yields

A building block of the topology graph.

Return type

Iterator[BuildingBlock]

get_num_building_block(building_block)

Get the number of times building_block is present.

Parameters

building_block (BuildingBlock) – The building block whose frequency in the topology graph is desired.

Return type

int

Returns

The number of times building_block is present in the topology graph.

with_building_blocks(building_block_map)

Return a clone holding different building blocks.

Parameters

building_block_map (dict[BuildingBlock, BuildingBlock]) – Maps a building block in the current topology graph to the building block which should replace it in the clone. If a building block should be not replaced in the clone, it can be omitted from the map.

Return type

TopologyGraph

Returns

The clone.

exception OverlyOccupiedVertexError[source]

Bases: Exception

When a COF vertex is occupied by more than one building block.

args
with_traceback()

Exception.with_traceback(tb) – set self.__traceback__ to tb and return self.

exception UnoccupiedVertexError[source]

Bases: Exception

When a COF vertex is not occupied by a building block.

args
with_traceback()

Exception.with_traceback(tb) – set self.__traceback__ to tb and return self.