(197k) Implementation of Genetic Algorithms to Optimize Metal-Organic Frameworks for CO2 Capture

Metal-organic frameworks (MOFs) are promising adsorbents for CO₂ capture that could be used in adsorption processes that are potentially less energy intensive than current methods for CO₂ capture using liquid amines. MOFs are highly versatile, and there are an almost unlimited number of MOFs that could be synthesized. A major challenge is to rapidly identify the most promising MOF materials for various application. In this work, we used genetic algorithms (GA) and grand canonical Monte Carlo (GCMC) simulations to efficiently search for high-performing MOFs for CO₂ capture. We determined and 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 GA on-the-fly to optimize MOFs using multiple objective functions across different topologies. GA was able to determine top-performing MOFs that expanded beyond the previously known pareto fronts and reduced the cost of molecular simulations by a factor of 25.