Rebecca L. Greenaway

Metal–organic frameworks (MOFs) are typically assembled from inflexible, 2D aromatic linker units to provide structural predictability, rigidity and prevent architectural collapse. Limiting the pool of structural units from which these materials are derived, however, inevitably restricts the diversity of architectures that can be realised. In this work, we have explored organic cages with 1,2,3‐triazole struts as 3D linkers for Ag(I)‐based MOFs. These linkers are unusual in two key facets. First, the cage structure is semi‐rigid, providing both shape persistence and (limited) conformational freedom. Second, in contrast to the reticular design concepts of traditional MOFs, minor structural modifications at locations remote from the coordinating units were found to induce profound changes to the resultant MOF architectures, which included 2D honeycomb structures, 2D corrugated sheets and an interpenetrated 3D network. This is the first report of the incorporation of reduced symmetry cage linkers into metal–organic cage‐to‐framework structures, providing a blueprint for the introduction of low‐symmetry and chiral intrinsic porosity into framework materials.

Metal–organic frameworks (MOFs) are typically assembled from inflexible, 2D aromatic linker units to provide structural predictability, rigidity and prevent architectural collapse. Limiting the pool of structural units from which these materials are derived, however, inevitably restricts the diversity of architectures that can be realised. In this work, we have explored organic cages with 1,2,3‐triazole struts as 3D linkers for Ag(I)‐based MOFs. These linkers are unusual in two key facets. First, the cage structure is semi‐rigid, providing both shape persistence and (limited) conformational freedom. Second, in contrast to the reticular design concepts of traditional MOFs, minor structural modifications at locations remote from the coordinating units were found to induce profound changes to the resultant MOF architectures, which included 2D honeycomb structures, 2D corrugated sheets and an interpenetrated 3D network. This is the first report of the incorporation of reduced symmetry cage linkers into metal–organic cage‐to‐framework structures, providing a blueprint for the introduction of low‐symmetry and chiral intrinsic porosity into framework materials.

The diversification of lead compounds remains a crucial step in drug development, with bioactive molecules often requiring fine tuning of a range of important pharmaceutical properties. This is achieved through the synthesis of libraries of analogues with subtle modifications in functionality or structure. The complexity of many bioactive molecules, however, does not make this a trivial endeavour. Indeed, many transformations using established chemistry are not suitable due to functional group sensitivity or regiochemical challenges and can require large amounts of time and resources to be spent on multistep syntheses. The development of functional group tolerant transformations can allow the curation of a library of analogues directly from a common, complex precursors through late-stage modification. Harnessing the reactivity of C–H bonds on arenes, which are extremely prevalent in drug like compounds, allows for the addition of functionality to tune electronic and steric properties without the modification of existing functional groups that may be integral to binding properties. Particularly attractive is the installation of a halogen atom to a lead compound to provide opportunities for further diversification through emerging and established cross-coupling techniques as well as the modulation of pharmaceutical properties. Numerous conditions are available to perform C–H halogenation reactions on aromatic compounds. However, “one-size-fits-all” conditions are not reflective of the structural diversity across pharmaceutically relevant molecules and cannot accommodate the density and breadth of functionality. High-throughput screening has been used to optimise C–H functionalisation reactions. However, no such approach exists for the halogenation of pharmaceutical compounds. Here, we develop an automated, machine-learning aided, high-throughput workflow to investigate how varying acidity affects the C(sp ² )–H chlorination, bromination, and iodination of 32 complex pharmaceutical substrates. Different compounds are shown to require significantly different acidities and yields are improved by optimisation of TFA equivalents. 56 halogenated analogues of 22 pharmaceuticals were prepared using conditions determined by the high-throughput workflow with significant benefit to yield. xTB atomic descriptors were used to predict regiochemical outcomes in a computationally inexpensive way. We envision that the workflow could be used for future similar investigations.

An organic cage-based porous ionic liquid (PIL) was constructed by functionalising a reduced cage core with ionic liquid functionality. The resulting PIL displayed melting behaviour, a glass phase, enhanced CO₂ uptake and permanent porosity.

Organic cages are a robust class of molecular hosts with a myriad of applications in materials science. Despite this, there has been a paucity of explorations into the modification of their properties via external functionalization. In this work, [n]rotaxanes featuring unoccupied organic cages as stopper components and a small 2,2′‐bipyridine macrocycle were constructed using the active metal template (AMT) approach. By exploiting a scrambling methodology, it was possible to synthesize cages with a defined number of interlocked components (n = 2–4). The gas uptake, solubility, and thermal properties of the interlocked systems were compared against those of their constituent, non‐interlocked components. In this manner, we were able to demonstrate the potential of exploiting the mechanical bond for modulating the physiochemical properties of these molecular materials.

By combining solubility prediction software and a size-exclusivity prediction algorithm with a down-selection procedure and experimental validation steps, a computational workflow has been developed for the discovery of porous liquid solutions.

From accelerating simulations and exploring chemical space, to experimental planning and integrating automation within experimental labs, artificial intelligence (AI) is changing the landscape of chemistry. We are seeing a significant increase in the number of publications leveraging these powerful data-driven insights and models to accelerate all aspects of chemical research. For example, how we represent molecules and materials to computer algorithms for predictive and generative models, as well as the physical mechanisms by which we perform experiments in the lab for automation. Here, we present ten diverse perspectives on the impact of AI coming from those with a range of backgrounds from experimental chemistry, computational chemistry, computer science, engineering and across different areas of chemistry, including drug discovery, catalysis, chemical automation, chemical physics, materials chemistry. The ten perspectives presented here cover a range of themes, including AI for computation, facilitating discovery, supporting experiments, and enabling technologies for transformation. We highlight and discuss imminent challenges and ways in which we are redefining problems to accelerate the impact of chemical research via AI.

Metal‐organic cages (MOCs) are a class of self‐assembled materials with promising applications in chemical purifications, sensing, and catalysis. Their potential is, however, hampered by challenges in the targeted design of MOCs with desirable properties. MOC discovery is thus often reliant on trial‐and‐error approaches and brute‐force manual screening, which are time‐consuming, costly, and material‐intensive. Translating the synthesis and property screening of MOCs to an automated workflow is therefore attractive, to both accelerate discovery and provide the datasets crucial for data‐led approaches to accelerate MOC discovery and to realize their targeted properties for specific applications. Here, an automated workflow for the streamlined assembly and property screening of MOCs was developed, incorporating automated high‐throughput screening of variables pertinent to MOC synthesis, data curation and automated analysis, and development of a host–guest assay to rapidly assess binding behavior. Computational modelling supplemented this automated experimental workflow for post priori rationalization of experimental outcomes. This study lays the groundwork for future large‐scale MOC screening: from a relatively modest screen of 24 precursor combinations under one set of reaction conditions, 3 clean MOC species were identified, and subsequent screening of their host–guest behavior highlighted trends in binding and the identification of potential applications in molecular separations.

Organic cages are a robust class of molecular hosts with a myriad of applications in materials science. Despite this, there has been a paucity of explorations into the modification of their properties via external functionalization. In this work, [n]rotaxanes featuring unoccupied organic cages as stopper components and a small 2,2’-bipyridine macrocycle were constructed using the active metal template approach. By exploiting a scrambling methodology, it was possible to synthesise cages with a defined number of interlocked components (n = 2-4). The gas uptake, solubility and thermal properties of the interlocked systems were compared against those of their constituent, non-interlocked components. In this manner, we were able to demonstrate the potential of exploiting the mechanical bond for modulating the physiochemical properties of these molecular materials.

Porous liquids are a sub-class of porous materials that combine permanent porosity, typically associated with solids, with the fluidity and fast mass-transfer capabilities of liquids, making them ideal candidates for gas storage and separation applications. One strategy to form porous liquids is the dissolution of discrete and permanently porous molecular species at relatively high concentrations in cavity-excluded solvents, thus introducing permanent porosity into the liquid in which it is dissolved and ensuring a solution of reasonable porosity is obtained. To access high-performance porous liquids for target applications, the selection of both the porous molecular species and the cavity-excluded solvent is key to ensuring the solvent is permanently excluded and the pore carrier is highly soluble. Finding new solvents that fit both these requirements is challenging, often resulting in a trial-and-error approach. While predictive data-driven models may be attractive, the youth of the porous liquid field currently limits the availability of data necessary to train robust models. Here, we present a computational workflow for the discovery of new porous liquid solutions combining solubility prediction software and a size-exclusivity prediction algorithm that correctly predicts size-exclusivity; this is followed by experimental validation with a representative system. Our workflow yielded size-excluded solvent and soluble porous organic cage pairs, leading to the realisation of a new porous liquid with enhanced methane uptake compared to previous systems discovered in a purely experimental high-throughput brute-force manner, highlighting the advantages of incorporating a computational workflow in the discovery of new porous liquids.