We report the generation and characterization of the most complete collection of metal-organic frameworks (MOFs) maintained and updated, for the first time, by the Cambridge Crystallographic Data Centre (CCDC). To set up this subset, we asked the question "what is a MOF?" and implemented a number of "look-for-MOF" criteria embedded within a bespoke Cambridge Structural Database (CSD) Python API workflow to identify and extract information of 69,666 MOF materials. The CSD MOF subset is updated regularly with subsequent MOF additions to the CSD, bringing a unique record for all researchers working in the area of porous materials around the world, whether to perform high-throughput computational screening for materials discovery or to have a global view over the existing structures in a single resource. Using this resource, we then developed and used an array of computational tools to remove residual solvent molecules from the framework pores of all the MOFs identified and went on to analyze geometrical and physical properties of non-disordered structures.

We demonstrate how machine-learning approaches can significantly speed up the way materials are characterized and designed at their molecular scale. Using a multi-level computational approach, we delineate key structural features in metalorganic frameworks (MOFs) that influence their mechanical properties. Importantly, we highlight the strength of artificial neural networks in producing MOFs with mechanical properties in a matter of seconds without the need for complex and time-consuming calculations or experiments. The results guide MOF researchers to assess and design structures with improved mechanical stability.

Nanoparticle encapsulation inside zirconium-based metal-organic frameworks (NP@MOF) is hard to control and the resulting materials often have non-uniform morphologies with NPs on the external surface of MOFs and NP aggregates inside the MOFs. In this work, we report the controlled encapsulation of gold nanorods (AuNRs) by a scu-topology Zr-MOF, via a room-temperature MOF assembly. This is achieved by functionalizing the AuNRs with polyethylene glycol (PEG) surface ligands, allowing them to retain colloidal stability in the precursor solution and to seed the MOF growth. Using this approach, we achieve core-shell yields exceeding 99%, tuning the MOF particle size via the solution concentration of AuNRs. The functionality of AuNR@MOFs is demonstrated by using the AuNRs as embedded probes for selective surface-enhanced Raman spectroscopy (SERS). The AuNR@MOFs are able to both take-up or block molecules from the pores, thereby facilitating highly-selective sensing at the AuNR ends. This proofof-principle study serves both to present the outstanding level of control in the synthesis as well as the high potential for AuNR@Zr-MOF composites for SERS.

Large-scale targeted exploration of metal–organic frameworks (MOFs) with characteristics such as specific surface chemistry or metal-cluster family has not been investigated so far.

Stimuli-responsive behaviors of flexible metal–organic frameworks (MOFs) make these materials promising in a wide variety of applications such as gas separation, drug delivery, and molecular sensing. Considerable efforts have been made over the last decade to understand the structural changes of flexible MOFs in response to external stimuli. Uniform pore deformation has been used as the general description. However, recent advances in synthesizing MOFs with non-uniform porous structures, i.e. with multiple types of pores which vary in size, shape, and environment, challenge the adequacy of this description. Here, we demonstrate that the CO 2 -adsorption-stimulated structural change of a flexible MOF, ZIF-7, is induced by CO 2 migration in its non-uniform porous structure rather than by the proactive opening of one type of its guest-hosting pores. Structural dynamics induced by guest migration in non-uniform porous structures is rare among the enormous number of MOFs discovered and detailed characterization is very limited in the literature. The concept presented in this work provides new insights into MOF flexibility.