stk.metal_complex.MetalComplex

class stk.metal_complex.MetalComplex(metals, ligands, reaction_factory=None, num_processes=1, optimizer=<stk._internal.optimizers.null.NullOptimizer object>, scale_multiplier=1.0)[source]

Bases: TopologyGraph

Represents a metal complex topology graph.

Notes

Subclass Implementation

Each subclass needs to define the attributes, _metal_vertex_prototypes and _ligand_vertex_prototypes, which are tuple of Vertex instances.

Examples

Subclass Implementation

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

Basic Construction

For most MetalComplex topology graphs, we first need to define a metal BuildingBlock, consisting of 1 atom and multiple functional groups

import stk

metal = stk.BuildingBlock(
    smiles='[Fe+2]',
    functional_groups=(
        stk.SingleAtom(stk.Fe(0, charge=2))
        for i in range(6)
    ),
    position_matrix=[[0, 0, 0]],
)

We also need to define an organic ligand BuildingBlock

# Define an organic linker with two functional groups.
bidentate = stk.BuildingBlock(
    smiles='C=NC/C=N/Br',
    functional_groups=[
        stk.SmartsFunctionalGroupFactory(
            smarts='[#6]~[#7X2]~[#35]',
            bonders=(1, ),
            deleters=(),
        ),
        stk.SmartsFunctionalGroupFactory(
            smarts='[#6]~[#7X2]~[#6]',
            bonders=(1, ),
            deleters=(),
        ),
    ],
)

Finally, we can create the MetalComplex.

complex = stk.ConstructedMolecule(
    topology_graph=stk.metal_complex.OctahedralLambda(
        metals=metal,
        ligands=bidentate,
    )
)

Suggested Optimization

For MetalComplex topologies, it is recommend to use the MCHammer optimizer.

import stk

metal = stk.BuildingBlock(
    smiles='[Fe+2]',
    functional_groups=(
        stk.SingleAtom(stk.Fe(0, charge=2))
        for i in range(6)
    ),
    position_matrix=[[0, 0, 0]],
)

bidentate = stk.BuildingBlock(
    smiles='C=NC/C=N/Br',
    functional_groups=[
        stk.SmartsFunctionalGroupFactory(
            smarts='[#6]~[#7X2]~[#35]',
            bonders=(1, ),
            deleters=(),
        ),
        stk.SmartsFunctionalGroupFactory(
            smarts='[#6]~[#7X2]~[#6]',
            bonders=(1, ),
            deleters=(),
        ),
    ]
)

complex = stk.ConstructedMolecule(
    topology_graph=stk.metal_complex.OctahedralLambda(
        metals=metal,
        ligands=bidentate,
        optimizer=stk.MCHammer(),
    ),
)

Construction with Multiple Metals & Ligands

When multiple metals or ligands are used, the metals and ligands parameters accept values of type dict, which specify the exact vertex each metal or ligand needs to be placed on.

import stk

metal = stk.BuildingBlock(
    smiles='[Fe+2]',
    functional_groups=(
        stk.SingleAtom(stk.Fe(0, charge=2))
        for i in range(6)
    ),
    position_matrix=[[0, 0, 0]],
)

bidentate1 = stk.BuildingBlock(
    smiles='C=NC/C=N/Br',
    functional_groups=[
        stk.SmartsFunctionalGroupFactory(
            smarts='[#6]~[#7X2]~[#35]',
            bonders=(1, ),
            deleters=(),
        ),
        stk.SmartsFunctionalGroupFactory(
            smarts='[#6]~[#7X2]~[#6]',
            bonders=(1, ),
            deleters=(),
        ),
    ],
)

# Define a second organic linker with two functional
# groups.
bidentate2 = stk.BuildingBlock(
    smiles='C=NC(C)(C)/C(C)=N/Br',
    functional_groups=[
        stk.SmartsFunctionalGroupFactory(
            smarts='[#6]~[#7X2]~[#35]',
            bonders=(1, ),
            deleters=(),
        ),
        stk.SmartsFunctionalGroupFactory(
            smarts='[#6]~[#7X2]~[#6]',
            bonders=(1, ),
            deleters=(),
        ),
    ],
)

# Build heteroleptic complex.
complex = stk.ConstructedMolecule(
    topology_graph=stk.metal_complex.OctahedralLambda(
        metals=metal,
        ligands={
            bidentate1: (0, 1),
            bidentate2: (2, ),
        },
    ),
)

Note that the valid vertex identifiers depend on the exact metal complex you are using. These are detailed in the docstring for that specific metal vertex topology graph.

Unsubstituted Metal Complexes

Some metal complex topologies represent metal complexes with unsubstituted metal sites. For example, BidentateSquarePlanar has all sites substituted and CisProtectedSquarePlanar is the equivalent metal complex with some unsubstituted sites

import stk

pd = stk.BuildingBlock(
    smiles='[Pd+2]',
    functional_groups=(
        stk.SingleAtom(stk.Pd(0, charge=2))
        for i in range(4)
    ),
    position_matrix=[[0, 0, 0]],
)

# Define a bidentate ligand with two functional groups.
bidentate_ligand = stk.BuildingBlock(
    smiles='NCCN',
    functional_groups=[
        stk.SmartsFunctionalGroupFactory(
            smarts='[#7]~[#6]',
            bonders=(0, ),
            deleters=(),
        ),
    ],
)

# Construct a cis-protected square planar metal complex.
complex = stk.ConstructedMolecule(
    topology_graph=stk.metal_complex.CisProtectedSquarePlanar(
        metals=pd,
        ligands=bidentate_ligand,
    ),
)

Initialize a MetalComplex.

Parameters:

metals (BuildingBlock | dict[BuildingBlock, tuple[int, ...]]) –
Can be a dict which maps the BuildingBlock instances to the indices of the vertices in _metal_vertex_prototypes it should be placed on.

Can also be a BuildingBlock instance, which should be placed on all _metal_vertex_prototypes on the topology graph.
ligands (BuildingBlock | dict[BuildingBlock, tuple[int, ...]]) –
Can be a dict which maps the BuildingBlock instances to the indices of the vertices in _ligand_vertex_prototypes it should be placed on.

Can also be a BuildingBlock instance, which should be placed on all _ligand_vertex_prototypes on the topology graph.
reaction_factory (Optional[ReactionFactory]) – The reaction factory to use for creating bonds between building blocks. If None, a DativeReactionFactory is used, which produces only dative bonds in any reactions done by this topology construction.
num_processes (int) – The number of parallel processes to create during construct().
optimizer (Optimizer) – Used to optimize the structure of the constructed molecule.
scale_multiplier (float) – Scales the positions of the vertices.

Methods

`clone`	Return a clone.
`construct`	Construct a `ConstructedMolecule`.
`get_building_blocks`	Yield the building blocks.
`get_num_building_block`	Get the number of times building_block is present.
`with_building_blocks`	Return a clone holding different building blocks.

clone()[source]

Return a clone.

Returns:: The clone.
Return type:: MetalComplex

construct()

Construct a ConstructedMolecule.

Returns:: The data describing the ConstructedMolecule.
Return type:: ConstructionResult

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.
Returns:: The number of times building_block is present in the topology graph.
Return type:: int

with_building_blocks(building_block_map)[source]

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.
Returns:: The clone.
Return type:: MetalComplex