Computational Screening and Design of Metal-Organic Frameworks for CO2 Separation from Flue Gas

Rising CO2 levels, largely from flue gas emissions, are a significant contributor to global climate change. Adsorption using Metal-Organic Frameworks (MOFs) offers a promising solution for CO2 capture due to their high surface area, tunable porosity, and selectivity. To streamline the discovery of efficient MOFs, we developed a high-throughput virtual screening (HTVS) pipeline by integrating Grand-Canonical Monte Carlo (GCMC) simulations, molecular modeling and machine learning. We screened a filtered subset of the CoREMOF database and a user-defined hypothetical MOF bank to identify candidates with high CO2 adsorption capacity and selectivity. This approach yielded several high-performing MOFs, including five from the CoREMOF dataset and a new structure from the hypothetical bank exceeding the performance of existing MOFs. Our findings highlight the complex relationship between MOF geometries and CO2 capture performance, emphasizing the importance of features like open metal sites and pore geometry. This computational framework accelerates MOF discovery and provides valuable insights for experimental synthesis. Future work will focus on expanding the hypothetical MOF dataset, improving simulation accuracy, and employing advanced optimization techniques to enhance the screening process.