Abstract
Metal–organic cages are macrocyclic structures that can possess an intrinsic void that can hold molecules for encapsulation, adsorption, sensing, and catalysis applications. As metal–organic cages may be comprised from nearly any combination of organic and metal-containing components, cages can form with diverse shapes and sizes, allowing for tuning toward targeted properties. Therefore, their near-infinite design space is almost impossible to explore through experimentation alone and computational design can play a crucial role in exploring new systems. Although high-throughput computational design and screening workflows have long been known as powerful tools in drug and materials discovery, their application in exploring metal–organic cages is more recent. We show examples of structure prediction and host–guest/catalytic property evaluation of metal–organic cages. These examples are facilitated by advances in methods that handle metal-containing systems with improved accuracy and are the beginning of the development of automated cage design workflows. We finally outline a scope for how high-throughput computational methods can assist and drive experimental decisions as the field pushes toward functional and complex metal–organic cages. In particular, we highlight the importance of considering realistic, flexible systems.
Highlights
Porous materials have been the focus of significant development in recent decades
We have outlined the recent progress in applying computational design processes and methods to metal–organic cages (MOCs)
Much of this progress includes the development of software for tackling the structure generation and diversity of metal-containing structures
Summary
Andrew Tarzia received his PhD in chemistry from the University of Adelaide in 2019. Since 2019, he has been a postdoctoral researcher in the Jelfs group at Imperial College London. The introduction of dynamics, including solvent, and complex interactions in the study of host–guest systems poses a great challenge for high-throughput computation and property prediction. Significant progress in both software and hardware has made the advancements we highlight below possible. We outline this article based on a description of MOC structures and recent solutions to the challenges of studying metal-containing systems (Section 2), an introduction of structure generation processes (Section 3), methods for predicting the outcomes and processes of MOC self-assembly (Sections 4 and 5), examples of evaluating MOC host–guest properties (Section 6), and an outlook into the future of MOC design (Section 7)
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