Sezgin Pelin, Keskin Seda
Department of Chemical and Biological Engineering, Koç University, Rumelifeneri Yolu, Sariyer, Istanbul 34450, Turkey.
J Phys Chem C Nanomater Interfaces. 2025 Jul 4;129(28):13089-13099. doi: 10.1021/acs.jpcc.5c02779. eCollection 2025 Jul 17.
As the number of synthesized and hypothetical metal-organic frameworks (MOFs) continues to grow, identifying the most selective adsorbents for CH/H separation through experimental or computational methods has become increasingly complex. This study integrates molecular simulations with machine learning (ML) to evaluate the CH/H separation performance of 126605 distinct types of MOFs. Grand canonical Monte Carlo (GCMC) simulations were performed to produce CH and H adsorption data for synthesized MOFs at various pressures, which were then used to train ML models incorporating structural, chemical, and energetic features of the MOFs. These ML models were subsequently transferred to hypothetical MOFs, enabling the rapid and accurate screening of promising adsorbents for CH/H separation. The top-performing MOFs were identified based on their CH/H selectivities, and their key structural and chemical characteristics were analyzed. Synthesized (hypothetical) MOFs having narrow pores and pyridine-, histidine-, and imidazole-based (carboxylate-, benzoate-, and cubane-based) linkers demonstrated high selectivities up to 85 (115) at 1 bar and 298 K. Our findings highlight the potential of MOFs as superior alternatives to traditional adsorbent materials for CH/H separation.
随着合成的和假设的金属有机框架（MOF）数量不断增加，通过实验或计算方法确定用于CH/H分离的最具选择性的吸附剂变得越来越复杂。本研究将分子模拟与机器学习（ML）相结合，以评估126605种不同类型MOF的CH/H分离性能。进行了巨正则蒙特卡罗（GCMC）模拟，以生成合成MOF在各种压力下的CH和H吸附数据，然后将这些数据用于训练包含MOF结构、化学和能量特征的ML模型。随后将这些ML模型应用于假设的MOF，从而能够快速准确地筛选出有前景的CH/H分离吸附剂。根据CH/H选择性确定了性能最佳的MOF，并分析了它们的关键结构和化学特征。具有窄孔以及基于吡啶、组氨酸和咪唑（基于羧酸盐、苯甲酸盐和立方烷）的连接体的合成（假设）MOF在1 bar和298 K下表现出高达85（115）的高选择性。我们的研究结果突出了MOF作为CH/H分离中传统吸附剂材料的优越替代品的潜力。
