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This review spotlights the role of atomic‐level modeling in research on metal‐organic frameworks (MOFs), especially the key methodologies of density functional theory (DFT), Monte Carlo (MC) simulations, and molecular dynamics (MD) simulations. The discussion focuses on how periodic and cluster‐based DFT calculations can provide novel insights into MOF properties, with a focus on predicting structural transformations, understanding thermodynamic properties and catalysis, and providing information or properties that are fed into classical simulations such as force field parameters or partial charges. Classical simulation methods, highlighting force field selection, databases of MOFs for high‐throughput screening, and the synergistic nature of MC and MD simulations, are described. By predicting equilibrium thermodynamic and dynamic properties, these methods offer a wide perspective on MOF behavior and mechanisms. Additionally, the incorporation of machine learning (ML) techniques into quantum and classical simulations is discussed. These methods can enhance accuracy, expedite simulation setup, reduce computational costs, as well as predict key parameters, optimize geometries, and estimate MOF stability. By charting the growth and promise of computational research in the MOF field, the aim is to provide insights and recommendations to facilitate the incorporation of computational modeling more broadly into MOF research.

 

Recently, metal–organic framework (MOF)-based polymeric substrates show promising performance in many engineering and technology fields. However, a commonly known drawback of MOF/polymer composites is MOF crystal encapsulation and reduced surface area. This work reports a facile and gentle strategy to produce self-supported MOF predominant hollow fiber mats. A wide range of hollow MOFs including MIL-53(Al)–NH 2 , Al-PMOF, and ZIF-8 are successfully fabricated by our synthetic method. The synthetic strategy combines atomic layer deposition (ALD) of metal oxides onto polymer fibers and subsequent selective removal of polymer components followed by conversion of remaining hollow metal oxides into freestanding MOF predominant hollow fiber structures. The hollow MOFs show boosted surface area, superb porosity, and excellent pore accessibility, and exhibit a significantly improved performance in CO 2 adsorption (3.30 mmol g −1 ), CO 2 /N 2 separation selectivity (24.9 and 21.2 for 15/85 and 50/50 CO 2 /N 2 mixtures), and catalytic removal of HCHO (complete oxidation of 150 ppm within 60 min).