Adsorption of Methane, Ethane and Their Equimolar Mixture in NIIC‐20‐Bu Metal‐Organic Framework from Grand Canonical Monte Carlo Simulations and Artificial Neural Networks

Abstract The adsorption of methane, ethane, and their equimolar mixture in the mesoporous NIIC‐20‐Bu metal–organic framework (MOF) is investigated utilizing molecular simulations and artificial neural networks. To the best of knowledge, this is the first computational study of small alkanes sorbed in this particular MOF. Grand Canonical Monte Carlo simulations provided the adsorption isotherms of the aforementioned alkanes in NIIC‐20‐Bu at different temperatures. The simulation findings are compared with existing experimental sorption measurements showing reasonable quantitative and qualitative agreement. Predictive models based on artificial neural networks are developed incorporating simulation data and available experimental measurements in the training phase to predict the sorption isotherms of methane, ethane, and their equimolar mixture in NIIC‐20‐Bu mesoporous material with the minimum computational cost. 3D density profiles of sorbed methane and ethane are computed based on their positions in the simulation box of NIIC‐20‐Bu, as obtained from GCMC simulations. Moreover, the analysis of the aforementioned profiles highlighted preferred localization domains, siting motifs and interesting segregation phenomena of the sorbed methane, ethane molecules as pure components or in their equimolar mixture within the mesoporous crystal. The present findings highlight the potential applications of NIIC‐20‐Bu as an efficient adsorbent material.