MOFGA

Genetic Algorithms Implementation in Metal-Organic Frameworks

Overview

mofga is a python module to perform genetic algorithms to optimize metal-organic framework (MOF)'s building blocks (including topology, organic linker and metal node). mofga was tested on optimizing MOFs for CO₂ capture. mofga can perform GA search usingr a single objective function or multiple objective functions. MOFGA uses a chromosome representation format based on ToBaCCo 3.0's definition of a template. A MOF chromosome is consisted of (1) a topology, (2) node(s) (both inorganic and organic node) and (3) edge(s).

General Workflow

Initial solutions are created using Latin Hypercube sampling, modified from skopt library. MOF crystal structures are created using ToBaCCo 3.0. Through ranking selection methods, top-performing MOFs are kept, or used as parents for new solutions. To create new solutions, genetic operators, including crossover and mutation, are used.

Installation and Dependencies

MOFGA is tested for Python 3.7. MOFGA requires the following packages (and their dependencies):

• ToBaCCo 3.0 - used to create a MOF crystal structure from a chromosome
• Skopt - used to create initial population using Latin Hypercube Sampling

To set up MOFGA, we recommend to install Anaconda. If you already have a conda environment for ToBaCCo, you can continue to use that environment, and only need to install Skopt using

conda install -c conda-forge scikit-optimize

For a new environment, run the following commands:

conda create -n MOFGA python=3.7
conda activate MOFGA
conda install -c conda-forge scikit-optimize networkx=2.2

Note: networkx is the prerequisite of ToBaCCo. We tested ToBaCCo 3.0 using networkx version 2.2. Other versions of networkx maybe compatible with ToBaCCo (see ToBaCCo 3.0 for more details)

Components

• GA_params.py: contains GA parameters, such as the number of MOFs per generation, number of MOFs in tournament selection, ranking probability and the mutation probability
• template_params.py: contains the chromosome representation and building block compatibility for each topology
• data: contains the labels of the templates, nodes and edges.

Usage

To test your GA installation, run following command:

python GA_test.py

This will run GA using the parameters in test_case/. GA will be performed with 15 GA generations, 96 MOFs per generation, 10% mutation probability and 95% ranking probability to optimize the selectivity of CO2/N2 at adsorption conditions. GA will be performed using a preconstructed objective file (objectives/alpha_ads_and_WC_CO2_data.csv) which contains (in order) (1) MOF chromosomes of MOFs with fof topology, (2) MOF names as created by ToBaCCo 3.0, (3) the CO2/N2 selectivity and (4) the CO2 working capacity. More details about this data file can be found in our publication. thus no MOF crystal structure will be created.

To run mofga:

1. Include your libraries of templates, nodes and edges in the templates, nodes and edges directory.
2. Run the script > read_vertex_info.py to automatically create a data file and template_params.py for your libraries of templates, nodes and edges by

python read_vertex_info.py

3. (Optional) Modify GA_params.py to set GA parameters.
4. Run GA_production.py to create an initial population using the following method

create_initial_population(bb_options, n_mof_per_generation, output_dir, tobacco_path, lhs_optimized='maximin', data_fname='data', create_MOF=True, template_info=template_info, tobacco_output=True, preconstruct_file=None)

This will create the initial MOF population in the specified > output_dir

5. Calculate the objective function(s), then create a file with the mof chromosome, mof name and objective(s).
6. Once the mof_objective.txt is create, create new population using

create_new_population(n_mof_per_generation, template_info, m_prob, r_prop, bb_options, output_dir, objective_fname, tobacco_path, data_fname='data', create_MOF=True, tobacco_output=True, preconstruct_file=None, restart=False)