跟着经济的疾速成长,人们越来越依赖能源。然而,近年来,能源危机逐渐加剧。作为清洁能源,甲烷的开发利用受到了广泛的关注。这项研究采用了大规模的计算筛选和机器学习来研究六种CH4气体二元混合物(H2/CH4,N2/CH4,O2/CH4,CO2/CH4,H2S/CH4,He/CH4)中数千种金属有机骨架(MOFs)的吸附和扩散特性用于甲烷纯化。首先,进行了单因素分析,以讨论吸附剂的性能指标与其特征描述符之间的关系。随后,四种机器学习方法被用来预测气体的扩散率/选择性,随着光梯度增强机(LGBM)算法的出现,扩散率的R2值为0.954,选择性为0.931。此外,LGBM算法与SHapley加法扩张(SHAP)技术相结合,定量分析每个MOF描述符的相对重要性,揭示了孔限制直径(PLD)是影响分子扩散率的最关键的结构描述符。最后,对于每个CH4混合物系统,确定了三个高性能MOF,并分析了高性能MOFs之间的共性,提出了仅涉及金属中心变化的三个设计原则的建议,有机接头,或拓扑结构。因此,这项工作揭示了对MOFs中不同二元混合物中CH4分离机制的微观见解。
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.