Metal-organic frameworks (MOFs) are an emerging class of materials heralded for their range of desirable properties and ability to interact with many compounds. These materials, with a crystalline structure composed of organic linkers and metallic nodes, have nearly limitless combinations, making MOFs valuable in fields as diverse as gas storage, water filtration, and drug delivery.

However, a nearly infinite search space can also make it challenging to find an MOF with specific sought-after properties. Researchers looking to develop MOFs for any given application face a daunting task akin to finding a needle in a haystack. Choudhuri et al. described computational approaches to efficiently probe their physical and chemical properties.

“We have conducted comprehensive computational studies on MOFs, and we aim to summarize the most popular computational methods used for MOF modeling,” said author Jingyun Ye. “Through examples, we demonstrate how these methods have been effectively applied to address a variety of challenges related to MOFs.”

The team highlighted three broad approaches: quantum mechanical (QM) methodologies, which excel at modeling electron-based interactions such as the formation and breaking of atomic bonds; molecular mechanical (MM) methodologies, which can handle other interatomic forces such as bending, stretching, and twisting of bonds; and hybrid QM-MM systems that attempt to leverage the advantages of both.

While hybrid methodologies have significant potential, they can often struggle at the boundary between traditional QM and MM realms. The authors are confident that emerging technologies can help to bridge the gap.

“Computational modeling, together with big data and machine learning, can synergize to revolutionize material discovery and design,” said Ye.

Source: “Computational quantum chemistry of metal-organic frameworks,” by Indrani Choudhuri, Jingyun Ye, and Donald G. Truhlar, Chemical Physics Reviews (2023). The article can be accessed at