Electronic Structure Modeling of Metal–Organic Frameworks

Abstract

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.

Machine learning the quantum-chemical properties of metal–organic frameworks for accelerated materials discovery

“…the discovery of MOFs suitable for H 2 storage, [12][13][14] CO 2 separation/capture, [15][16][17] and numerous other applications predominantly (though not exclusively) 18,19 in the area of gas storage and separations. 10,20,21 Nonetheless, similar efforts remain almost entirely unexplored for the many applications in which the properties of interest are best described by quantum mechanical models, 22 such as those based on the electronic, optical, magnetic, and/or catalytic properties of MOFs. Beyond the sheer number of possible MOFs that can be realized, the large number of atoms in MOF crystal structures often makes it computationally demanding to carry out even moderate-scale quantum-chemical screening studies, further magnifying the need for ML approaches in this area.…”

Computational determination of coordination structure impact on adsorption and acidity of pristine and sulfated MOF-808

“…Surprisingly, while thousands of MOFs have been experimentally synthesized, there are only a few computational studies that have systematically examined MOF coordination structures and resulting properties. 9 For example, density functional calculations in combination with experiments revealed the thermodynamically most stable proton-topology of MOF NU-1000. 10 MOF-808 is a Zr-based mesoporous material that has relatively high thermal and mechanical stability, 5 which prompted structure modification and multiple studies in the application of adsorption, gas separation, and catalysis.…”

Machine Learning the Quantum-Chemical Properties of Metal–Organic Frameworks for Accelerated Materials Discovery with a New Electronic Structure Database

“…the discovery of MOFs suitable for H 2 storage, [12][13][14] CO 2 separation/capture, [15][16][17] and numerous other applications predominantly (though not exclusively) 18,19 in the area of gas storage and separations. 10,20,21 Nonetheless, similar efforts remain almost entirely unexplored for the many applications in which the properties of interest are best described by quantum mechanical models, 22 such as those based on the electronic, optical, magnetic, and/or catalytic properties of MOFs. Beyond the sheer number of possible MOFs that can be realized, the large number of atoms in MOF crystal structures often makes it computationally demanding to carry out even moderate-scale quantum-chemical screening studies, further magnifying the need for ML approaches in this area.…”