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

Metal-organic frameworks (MOFs) are promising materials for CO₂ capture with the potential to use less energy than current industrial CO₂ capture methods. MOFs are highly versatile sorbents, and there is an almost unlimited number of MOFs that could be synthesized. In this work, we used a genetic algorithm (GA) and grand canonical Monte Carlo (GCMC) simulations to efficiently search for high-performing MOFs for CO₂ capture. We analyzed the effects of important GA parameters, including the mutation probability, the number of MOFs per generation, and the number of GA generations, on the GA performance. We performed GCMC simulations on-the-fly during the GA procedure to determine the performance of proposed MOFs and optimized their structures using multiple objective functions across different topologies. The GA was able to determine top-performing MOFs balancing CO₂ selectivity versus working capacity and reduced the cost of molecular simulations by a factor of 25 versus brute-force screening of an entire database of structures.