Rapid Screening of Metal–Organic Frameworks for Propane/Propylene Separation by Synergizing Molecular Simulation and Machine Learning

At present, 100 000+ metal-organic frameworks (MOFs) have been synthesized, and it is challenging to identity the best candidate for a specific application. In this study, MOFs are rapidly screened via a hierarchical approach for propane/propylene (C3H8/C3H6) separation. First, the adsorption capacity and selectivity of C3H8/C3H6 mixture in "Computation-Ready, Experimental" (CoRE) MOFs are predicted via a molecular simulation (MS) method. The relationships between separation metrics and structural factors are established, and top-performing CoRE MOFs are identified. Then, machine learning (ML) models are trained and developed upon the CoRE MOFs using pore size, pore geometry, and framework chemistry as feature descriptors. By introducing binned pore size distributions and geometric descriptors, the accuracy of ML models is substantially improved. The feature importance of the descriptors is physically interpreted by the Gini impurities and Shapley Additive Explanations. Subsequently, the ML models are used to rapidly screen experimental "Cambridge Structural Database" (CSD) MOFs and hypothetical MOFs for C3H8/C3H6 separation. In the CSD MOFs, the out-of-sample predictions are found to agree well with simulation results, demonstrating the excellent transferability of the ML models from the CoRE to CSD MOFs. Moreover, nine CSD MOFs are identified to possess separation performance superior to top-performing CoRE MOFs. Finally, the similarity and diversity among experimental and hypothetical MOFs are visualized and compared by the t-Distributed Stochastic Neighbor Embedding (t-SNE) feature projections. Remarkably, the CoRE and CSD MOFs are revealed to share a close similarity in both chemical and geometric feature spaces. By synergizing MS and ML, the hierarchical approach developed in this study would advance the rapid screening of MOFs across different databases toward industrially important separation processes.