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.

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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.

This communication briefly reviews why network topology is an important tool (for understanding, comparing, communicating, designing, and solving crystal structures from powder diffraction data) and then discusses the terms of an IUPAC project dealing with various aspects of network topology. One is the ambiguity in node assignment, and this question is addressed in more detail. First, we define the most important approaches: the “all node” deconstruction considering all branch points of the linkers, the “single node” deconstruction considering only components mixed, and the ToposPro “standard representation” also considering linkers as one node but, if present, takes each metal atom as a separate node. These methods are applied to a number of metal–organic framework structures (MOFs, although this is just one example of materials this method is applicable on), and it is concluded that the “all node” method potentially yields more information on the structure in question but cannot be recommended as the only way of reporting the network topology. In addition, several terms needing definitions are discussed.

A tutorial review for mining the ever growing number of metal–organic frameworks data in the Cambridge Structural Database, for MOF scientists of all backgrounds.

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Optimizing structures with computations on clusters of molecules permits generation of structure-specific restraints for refinement. Equally importantly, retrospective structure validation and addition of hydrogen atoms consistent with quantum chemistry is...

The Cambridge Structural Database (CSD) is the world's largest and most comprehensive collection of organic, organometallic, and metal-organic crystal structure information. Analyses using the data have wide impact across the chemical sciences in allowing understanding of structural preferences. In this short review, we illustrate the more common methods by which CSD data influence molecular design. We show how more data could lead to more refined insights into the future using a simple example of trifluoromethylphenyl fragments, highlighting how with sufficient data one can build a reasonable model of geometric change in a chemical fragment with torsional rotation, and show some recent examples where the CSD has been used in conjunction with other methods to provide design ideas and more computationally tractable workflows for derivation of useful insights into structural design.