Separating and capturing small amounts of CH₄ or H₂ from a mixture of gases, such as coal mine spent air, at a large scale remains a great challenge. We used large-scale computational screening and machine learning (ML) to simulate and explore the adsorption, diffusion, and permeation properties of 6013 computation-ready experimental metal–organic framework (MOF) adsorbents and MOF membranes (MOFMs) for capturing clean energy gases (CH₄ and H₂) in air. First, we modeled the relationships between the adsorption and the MOF membrane performance indicators and their characteristic descriptors. Among three ML algorithms, the random forest was found to have the best prediction efficiency for two systems (CH₄/(O₂ + N₂) and H₂/(O₂ + N₂)). Then, the algorithm was further applied to quantitatively analyze the relative importance values of seven MOF descriptors for five performance metrics of the two systems. Furthermore, the 20 best MOFs were also selected. Finally, the commonalities between the high-performance MOFs were analyzed, leading to three types of material design principles: tuned topology, alternative metal nodes, and organic linkers. As a result, this study provides microscopic insights into the capture of trace amounts of CH₄ or H₂ from air for applications involving coal mine spent air and hydrogen leakage.

With the rapid growth of the economy, people are increasingly reliant on energy sources. However, in recent years, the energy crisis has gradually intensified. As a clean energy source, methane has garnered widespread attention for its development and utilization. This study employed both large-scale computational screening and machine learning to investigate the adsorption and diffusion properties of thousands of metal–organic frameworks (MOFs) in six gas binary mixtures of CH₄ (H₂/CH₄, N₂/CH₄, O₂/CH₄, CO₂/CH₄, H₂S/CH₄, He/CH₄) for methane purification. Firstly, a univariate analysis was conducted to discuss the relationships between the performance indicators of adsorbents and their characteristic descriptors. Subsequently, four machine learning methods were utilized to predict the diffusivity/selectivity of gas, with the light gradient boosting machine (LGBM) algorithm emerging as the optimal one, yielding R² values of 0.954 for the diffusivity and 0.931 for the selectivity. Furthermore, the LGBM algorithm was combined with the SHapley Additive exPlanation (SHAP) technique to quantitatively analyze the relative importance of each MOF descriptor, revealing that the pore limiting diameter (PLD) was the most critical structural descriptor affecting molecular diffusivity. Finally, for each system of CH₄ mixture, three high-performance MOFs were identified, and the commonalities among high-performance MOFs were analyzed, leading to the proposals of three design principles involving changes only to the metal centers, organic linkers, or topological structures. Thus, this work reveals microscopic insights into the separation mechanisms of CH₄ from different binary mixtures in MOFs.