Probing the structure, stability and hydrogen adsorption of lithium functionalized isoreticular MOF-5 (Fe, Cu, Co, Ni and Zn) by density functional theory

Metal-Organic Frameworks through the Lens of Artificial Intelligence: A Comprehensive Review

Metal-organic frameworks (MOFs) are a class of hybrid porous materials that have gained prominence as a noteworthy material with varied applications. Currently, MOFs are in extensive use, particularly in the realms of energy and catalysis. The synthesis of these materials poses considerable challenges, and their computational analysis is notably intricate due to their complex structure and versatile applications in the field of material science. Density functional theory (DFT) has helped researchers in understanding reactions and mechanisms, but it is costly and time-consuming and requires bigger systems to perform these calculations. Machine learning (ML) techniques were adopted in order to overcome these problems by implementing ML in material data sets for synthesis, structure, and property predictions of MOFs. These predictions are fast, efficient, and accurate and do not require heavy computing. In this review, we discuss ML models used in MOF and their incorporation with artificial intelligence (AI) in structure and property predictions. The advantage of AI in this field would accelerate research, particularly in synthesizing novel MOFs with multiple properties and applications oriented with minimum information.

Electronic Structure Modeling of Metal-Organic Frameworks

Owing to their molecular building blocks, yet highly crystalline nature, metal-organic frameworks (MOFs) sit at the interface between molecule and material. Their diverse structures and compositions enable them to be useful materials as catalysts in heterogeneous reactions, electrical conductors in energy storage and transfer applications, chromophores in photoenabled chemical transformations, and beyond. In all cases, density functional theory (DFT) and higher-level methods for electronic structure determination provide valuable quantitative information about the electronic properties that underpin the functions of these frameworks. However, there are only two general modeling approaches in conventional electronic structure software packages: those that treat materials as extended, periodic solids, and those that treat materials as discrete molecules. Each approach has features and benefits; both have been widely employed to understand the emergent chemistry that arises from the formation of the metal-organic interface. This Review canvases these approaches to date, with emphasis placed on the application of electronic structure theory to explore reactivity and electron transfer using periodic, molecular, and embedded models. This includes (i) computational chemistry considerations such as how functional, k-grid, and other model variables are selected to enable insights into MOF properties, (ii) extended solid models that treat MOFs as materials rather than molecules, (iii) the mechanics of cluster extraction and subsequent chemistry enabled by these molecular models, (iv) catalytic studies using both solids and clusters thereof, and (v) embedded, mixed-method approaches, which simulate a fraction of the material using one level of theory and the remainder of the material using another dissimilar theoretical implementation.

DFT studies on storage and adsorption capacities of gases on MOFs

Metal-organic frameworks (MOFs) are highly porous crystalline materials, consisting of metal ions linked together with organic bridging ligands, exhibiting high surface areas. Lately, they have been utilized for gas sorption, storage, sensing, drug delivery, etc. The chemistry of MOFs is expanding with an extraordinary speed, constituting both theoretical and experimental research, and MOFs have proved to be promising candidates so far. In this work, we have reviewed the density functional theory studies of MOFs in the adsorption and separation of the greenhouse gas, CO2, as well as the storage efficiencies for fuel gases like H2, CH4 and C2H2. The role of organic ligands, doping with other metal ions and functional groups, open metal sites and hybrid MOFs have been reviewed in brief.

Tetracyclo(9-methyl-2,7-carbazole) as a promising nanohoop for gas trapping: a multiscale study

H₂S, CS₂, NO₂, Br₂, HF, and C₂H₆ are the ideal adsorbates within the TCC host from their respective congeners.

Cobalt doping of the MOF-5 framework and its effect on gas-adsorption properties

Partial isomorphic substitution of Zn in IRMOF metal clusters by cobalt ions is described for the first time. Specifically, different numbers of Co(2+) ions have been incorporated during solvothermal crystallization into the Zn-based MOF-5 (IRMOF-1) framework, which is one of the most studied MOF materials. The amount of Zn that can be substituted seems to be limited, being no more than 25% of total metal content, that is, no more than one Co atom inside every metal cluster formed by four transition-metal ions, on average. Several characterization techniques, including X-ray diffraction, DR UV-visible spectroscopy, N(2) adsorption isotherms, and thermogravimetrical analysis, strongly support the effective incorporation of Co into the material framework. As-synthesized CoMOF-5 has cobalt ions in octahedral coordination, changing to tetrahedral by simple evacuation, presumably by the removal of two diethylformamide molecules per Co ion. Moreover, the H(2), CH(4), and CO(2) uptake of MOF-5 materials systematically increases with the Co content, particularly at high pressure. Such an increase is moderate anyway, considering that Co is incorporated into unexposed metal sites that are less accessible to gas molecules.