In Silico Study of Metal–Organic Frameworks for CO2/CO Separation: Molecular Simulations and Machine Learning

Metal–organic frameworks (MOFs), an emerging class of nanoporous materials, have drawn considerable attention as promising adsorbents for gas separations. Among various separation applications, CO2/CO separation is of particular interest owing to its industrial relevance. While searching for promising MOFs from tens of thousands of candidates represents a great challenge, this study conducts large-scale molecular simulations to identify top-performing CO2 adsorbents, followed by investigating structure–property relationships for their design. Optimal MOFs are found to possess features such as metal nodes of greater metallic charges and dipole moments with a relatively confined pore structure. With the large-scale data at our disposal, machine learning models capable of predicting the CO2-to-CO selectivity and adsorption uptakes are also established. Specifically, three algorithms including support vector regression (SVR), extreme gradient boosting (XGBoost), and random forest (RF) models are employed. The results show that the RF algorithm demonstrates the best accuracy, and the r value for the predicted CO2-to-CO selectivity (S) can be as large as ∼0.88. The relative importance of the adopted features is also investigated with results suggesting that the adsorption of CO2 initiates more preferentially than that of CO due to the stronger van der Waals interaction and electrostatic contribution between CO2 and the metal sites. Finally, a design rule is proposed for the optimal design of CO2-selective materials. Overall, this work demonstrates a successful hybrid approach combining molecular simulations and machine learning for screening highly CO2/CO selective MOFs and offering insights into the design of optimal adsorbents.