AI‐Aided High‐Throughput Screening and Optimistic Design of MOF Materials for Adsorptive Gas Separation

In industry, the separation/purification of gas mixtures is of significant importance. Adsorption separation is an effective route for this application regarding energy cost and separation efficiency, for which the selection of adsorbents is the key. This chapter introduces a computer-aided high-throughput screening strategy for the selection of metal–organic frameworks (MOFs) for gas separation, striving to accelerate the practice of discovering better adsorbents for particular applications. The practice usually consists of several steps. Firstly, prescreen of predetermined potential MOF candidates based on intuitive structure and chemical analysis. Secondly, molecular simulation-assisted structure–property relationship analysis is conducted based on small sampled MOFs, from which structure criteria can be derived for high-performing MOFs. Thirdly, a screening procedure is carried out to select the desired MOFs according to the derived criteria. Fourthly, rigorous molecular simulation is applied to assess the separation performance metrics of the selectied MOFs. By analyzing the simulated performance indicators, top-performing MOFs can be identified. Last but not least, the impact of practical factors, such as humidity, operation temperature, and inlet gas composition, on the performance of the selected adsorbents in practice is explored.