Deep learning and big data mining for Metal–Organic frameworks with high performance for simultaneous desulfurization and carbon capture.

[Display omitted] Carbon capture and desulfurization of flue gases are crucial for the achievement of carbon neutrality and sustainable development. In this work, the "one-step" adsorption technology with high-performance metal–organic frameworks (MOFs) was proposed to simultaneously capture the SO 2 and CO 2. Four machine learning algorithms were used to predict the performance indicators (N CO2+SO2 , S CO2+SO2/N2 , and TSN) of MOFs, with Multi-Layer Perceptron Regression (MLPR) showing better performance (R 2 = 0.93). To address sparse data of MOF chemical descriptors, we introduced the Deep Factorization Machines (DeepFM) model, outperforming MLPR with a higher R 2 of 0.95. Then, sensitivity analysis was employed to find that the adsorption heat and porosity were the key factors for SO 2 and CO 2 capture performance of MOF, while the influence of open alkali metal sites also stood out. Furthermore, we established a kinetic model to batch simulate the breakthrough curves of TOP 1000 MOFs to investigate their dynamic adsorption separation performance for SO 2 /CO 2 /N 2. The TOP 20 MOFs screened by the dynamic performance highly overlap with those screened by the static performance, with 76 % containing open alkali metal sites. This integrated approach of computational screening, machine learning, and dynamic analysis significantly advances the development of efficient MOF adsorbents for flue gas treatment. [ABSTRACT FROM AUTHOR]