lipelopesoliveira / pyCOFBuilder

A package for Covalent Organic Frameworks structure assembly based on specific building block, topology and functional groups based on the reticular approach
https://lipelopesoliveira.github.io/pyCOFBuilder/
MIT License
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computational-chemistry covalent-organic-frameworks machine-learning reticular-chemistry

pyCOFBuilder

puCOFBuilder

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What is pyCOFBuilder?

pyCOFBuilder is a simple and powerful python package to automatically assembly COF structures with specifics building blocks, topologies, and functionalizations following the reticular approach to build and represent COF structures. The project was developed to address the need for generation of COFs structures in a high-throughput style, based on a nomenclature tha allows direct sctructural feature interpretation from a simple name. The package uses pymatgen to create the structures.

This package is still under development and, but it is already possible to create a large number of COFs structures.

Learn more at the Documentation

Requirements

  1. Python >= 3.10
  2. pymatgen >= 2022.0.0
  3. numpy >= 1.2
  4. scipy >= 1.6.3
  5. simplejson
  6. ase
  7. gemmi

The Python dependencies are most easily satisfied using a conda (anaconda/miniconda) installation by running

conda env create --file environment.yml

Installation

You can install pyCOFBuilder using pip:

pip install pycofbuilder

Alternativelly, you can use pyCOFBuilder by manually import it using the sys module, as exemplified below:

# importing module
import sys

# appending a path
sys.path.append('{PATH_TO_PYCOFBUILDER}/pyCOFBuilder/src')

import pycofbuilder as pcb

Just remember to change the {PATH_TO_PYCOFBUILDER} to the directory where you download the pyCOFBuilder package.

Basic Usage

To create a specific COF, such as T3_BENZ_NH2_OH-L2_BENZ_CHO_H-HCB_A-AA:

# importing module
import sys

# appending a path
sys.path.append('{PATH_TO_PYCOFBUILDER}/pyCOFBuilder/src')

import pycofbuilder as pcb

cof = pcb.Framework('T3_BENZ_CHO_OH-L2_BENZ_NH2_H-HCB_A-AA')
cof.save(fmt='cif', supercell = [1, 1, 2], save_dir = '.')

You should see an output such as:

T3_BENZ_NH2_OH-L2_BENZ_CHO_H_H-HCB_A-AA                       hexagonal   P    P6/m # 175    12 sym. op.

A .cif file (the default save format is CIF, but it can be easily changed by setting other value on the fmt option) will be created in the out folder. The code will print out some information about the structure created.

Currently, it is possible to select the following formats:

Besides, the variable structure now is a Framework object. This object has some attributes that can be accessed:

>>> cof.name
'T3_BENZ_NH2_OH-L2_BENZ_CHO_H-HCB_A-AA'
>>> cof.smiles
'(N)C1=C(O)C((N))=C(O)C((N))=C1O.(C([H])=O)C1=C([H])C([H])=C((C([H])=O))C([H])=C1[H]'
>>> cof.lattice
array([[ 22.49540055,   0.        ,   0.        ],
       [-11.24770028,  19.48158835,   0.        ],
       [  0.        ,   0.        ,   3.6       ]])
>>> cof.n_atoms
72
>>> cof.space_group
'P6/m'

COFs and Building Blocks nomenclature

In order to ensure greater reproducibility as well as quickly and easily access to relevant information from the COFs, I've developed a simple nomenclature to name the structure. Generally speaking, a COF can be described as

Building_Block_1-Building_Block_2-Net-Stacking

where:

To name the building blocks I also developed a set of rules. The building block can be described as

Symmetry_Core_Connector_RadicalGroupR1_RadicalGroupR2_RadicalGroupR3_...

where:

Note that every "card" for the building block name is separated by an underline (_) and every "card" for the COF name is separated by a dash (-). This makes it easy to split the COF name into useful information.

Current available Building Blocks

Ditopic Ditopic Tritopic Tetratopic Hexatopic

Current available Connector Groups

Connection groups

Current available R Groups

Functional Groups

Citation

If you find pyCOFBuilder useful in your research please consider citing the following paper:

F. L. Oliveira and P. M. Esteves, pyCOFBuilder: A python package for automated creation of Covalent Organic Framework models based on the reticular approach J. Chem. Inf. Model. 2024, 64, 8, 3278–3289 10.1021/acs.jcim.3c01918 DOI