High-Throughput Screening of Metal–Organic Frameworks Assisted by Machine Learning: Propane/Propylene Separation

The separation of a propane (C 3 H 8 )/propylene(C 3 H 6 ) mixture is of paramount importance in the petrochemical industry. Metal–organic frameworks (MOFs), as a class of promising alternative to the traditional adsorbents, have garnered extensive interest. This study proposes a machine learning-assisted high-throughput screening strategy for the identification of suitable MOFs for C 3 H 8 /C 3 H 6 separation, striving to accelerate the discovery of high-performance MOF candidates for this particular application. First, a chemical/geometric analysis-based prescreening is applied to a data set of 146 203 MOFs composed of an experimentally synthesized MOF database and a hypothetical MOF database, and MOFs with undesirable chemical/geometric features were excluded. Six structural and nine chemical descriptors were calculated for the remaining MOFs. Random Forest regression algorithm was applied to “learn” the relationship correlations between the features (chemical and/or structural) of MOFs and their C 3 H 8 /C 3 H 6 separation capacity. Grand Canonical Monte Carlo (GCMC) simulations were applied to evaluate the C 3 H 8 /C 3 H 6 separation performances of the randomly selected training and testing MOF samples. A performance prediction model based on chemical and structural descriptors was obtained with R 2 equal to 0.96, which was employed for a separation performance prediction of the remaining MOFs. 2500 MOFs with potential to possess high C 3 H 8 /C 3 H 6 separation performance were shortlisted by the prediction model. GCMC simulations were applied to calibrate the prediction results and validate of the machine learning model. MOFs with competitively high C 3 H 8 /C 3 H 6 separation potential and regenerability were identified, and a comparison with MOFs reported in the literature was made, indicating that the proposed machine learning-assisted high-throughput screening approach is efficient and effective. Furthermore, structure–property correlation analysis was conducted.