MOFGA | Thang D. Pham

MOFGA is a Python framework for optimizing metal–organic framework (MOF) building blocks for target properties. It represents each candidate as a combination of topology, metal and organic nodes, and linkers, then searches the resulting design space with selection, crossover, and mutation. The framework supports both single- and multi-objective optimization and uses ToBaCCo to construct candidate crystal structures.

MOFGA was developed and tested for identifying MOFs with strong carbon-capture performance, including high CO₂/N₂ selectivity and CO₂ working capacity (Pham & Snurr, 2025).

Stack: Python, scikit-optimize, ToBaCCo.
Code: github.com/snurr-group/mofga

References

2025

Implementation of Genetic Algorithms to Optimize Metal-Organic Frameworks for CO2 Capture

Thang D. Pham and Randall Q. Snurr

Langmuir, 2025

DOI Bib HTML

@article{pham_implementation_2025,
  title = {Implementation of Genetic Algorithms to Optimize Metal-Organic Frameworks for CO2 Capture},
  author = {Pham, Thang D. and Snurr, Randall Q.},
  journal = {Langmuir},
  volume = {41},
  number = {7},
  pages = {4585--4593},
  year = {2025},
  publisher = {American Chemical Society},
  doi = {10.1021/acs.langmuir.4c04386},
}