Daglar Hilal, Gulbalkan Hasan Can, Aksu Gokhan Onder, Keskin Seda
Department of Chemical and Biological Engineering, Koç University, Rumelifeneri Yolu, Sariyer, Istanbul, 34450, Turkey.
Adv Mater. 2024 Jul 27:e2405532. doi: 10.1002/adma.202405532.
Metal-organic frameworks (MOFs), renowned for their exceptional porosity and crystalline structure, stand at the forefront of gas adsorption and separation applications. Shortly after their discovery through experimental synthesis, computational simulations quickly become an important method in broadening the use of MOFs by offering deep insights into their structural, functional, and performance properties. This review specifically addresses the pivotal role of molecular simulations in enlarging the molecular understanding of MOFs and enhancing their applications, particularly for gas adsorption. After reviewing the historical development and implementation of molecular simulation methods in the field of MOFs, high-throughput computational screening (HTCS) studies used to unlock the potential of MOFs in CO capture, CH storage, H storage, and water harvesting are visited and recent advancements in these adsorption applications are highlighted. The transformative impact of integrating artificial intelligence with HTCS on the prediction of MOFs' performance and directing the experimental efforts on promising materials is addressed. An outlook on current opportunities and challenges in the field to accelerate the adsorption applications of MOFs is finally provided.
金属有机框架材料(MOFs)以其卓越的孔隙率和晶体结构而闻名,在气体吸附和分离应用领域处于前沿地位。通过实验合成发现它们后不久,计算模拟迅速成为拓展MOFs用途的重要方法,它能深入洞察MOFs的结构、功能和性能特性。本综述特别探讨了分子模拟在加深对MOFs的分子理解以及增强其应用方面的关键作用,尤其是在气体吸附方面。在回顾了MOFs领域分子模拟方法的历史发展和应用之后,介绍了用于挖掘MOFs在CO捕获、CH存储、H存储和水收集方面潜力的高通量计算筛选(HTCS)研究,并突出了这些吸附应用的最新进展。阐述了将人工智能与HTCS相结合对预测MOFs性能以及指导有前景材料的实验工作的变革性影响。最后展望了该领域当前在加速MOFs吸附应用方面的机遇和挑战。