Topological landscapes of porous organic cages

Predisposition in Dynamic Covalent Chemistry: The Role of Non-Covalent Interactions in the Assembly of Tetrahedral Boronate Cages

Directional bonding strategies guide the design of complex molecular architectures, yet challenges arise due to emergent behavior. Rigid structures face geometric constraints and sensitivity to mismatches, hindering the efficient assembly of molecular organic cages (MOCs). Harnessing intramolecular non-covalent interactions offers a promising solution, broadening geometrical possibilities and enhancing adaptability to boost assembly yields. However, identifying these interactions remains challenging, with their full potential sometimes latent until final assembly. This study explores these challenges by synthesizing boronic acid tripods with varied oxygen positions at the tripodal feet and investigating their role in assembling tetrahedral boronate MOCs. Our results reveal substantial differences in the assembly efficiency among tripods. While the building blocks with oxygen in the benzylic position relative to the central aromatic ring form the MOCs in high yields, those with the oxygen atom directly bound to the central aromatic ring, only yield traces. Through X-ray crystallography and DFT analyses, we elucidate how intramolecular interactions profoundly influence the geometry of the building blocks and cages in a relay-like fashion, highlighting the importance of considering intramolecular interactions in the rational design of (supra)molecular architectures.

Argentophilic Interactions, Flexibility, and Dynamics of Pyrrole Cages Encapsulating Silver(I) Clusters

Recently, pyrrole cages have been synthesized that encapsulate ion pairs and silver(I) clusters to form intricate supramolecular capsules. We report here a computational analysis of these structures using density functional theory combined with a semiempirical tight-binding approach. We find that for neutral pyrrole cages, the Gibbs free energies of formation provide reliable predictions for the ratio of bound ions. For charged pyrrole cages, we find strong argentophilic interactions between Ag ions on the basis of the calculated bond indices and molecular orbitals. For the cage with the Ag4 cluster, we find two minimum-geometry conformations that differ by only 6.5 kcal/mol, with an energy barrier <1 kcal/mol, suggesting a very flexible structure as indicated by molecular dynamics. The predicted energies of formation of [Agn⊂1]n-3+ (n = 1-5) cryptands provide low energy barriers of formation of 5-20 kcal/mol for all cases, which is consistent with the experimental data. Furthermore, we also examined the structural variability of mixed-valence silver clusters to test whether additional geometrical conformations inside the organic cage are thermodynamically accessible. In this context, we show that the time-dependent density functional theory UV-vis spectra may potentially serve as a diagnostic probe to characterize mixed-valence and geometrical configurations of silver clusters encapsulated into cryptands.

Streamlining the automated discovery of porous organic cages

Self-assembly through dynamic covalent chemistry (DCC) can yield a range of multi-component organic assemblies. The reversibility and dynamic nature of DCC has made prediction of reaction outcome particularly difficult and thus slows the discovery rate of new organic materials. In addition, traditional experimental processes are time-consuming and often rely on serendipity. Here, we present a streamlined hybrid workflow that combines automated high-throughput experimentation, automated data analysis, and computational modelling, to accelerate the discovery process of one particular subclass of molecular organic materials, porous organic cages. We demonstrate how the design and implementation of this workflow aids in the identification of organic cages with desirable properties. The curation of a precursor library of 55 tri- and di-topic aldehyde and amine precursors enabled the experimental screening of 366 imine condensation reactions experimentally, and 1464 hypothetical organic cage outcomes to be computationally modelled. From the screen, 225 cages were identified experimentally using mass spectrometry, 54 of which were cleanly formed as a single topology as determined by both turbidity measurements and 1H NMR spectroscopy. Integration of these characterisation methods into a fully automated Python pipeline, named cagey, led to over a 350-fold decrease in the time required for data analysis. This work highlights the advantages of combining automated synthesis, characterisation, and analysis, for large-scale data curation towards an accessible data-driven materials discovery approach.

Programmable synthesis of organic cages with reduced symmetry

Integrating symmetry-reducing methods into self-assembly methodology is desirable to efficiently realise the full potential of molecular cages as hosts and catalysts. Although techniques have been explored for metal organic (coordination) cages, rational strategies to develop low symmetry organic cages remain limited. In this article, we describe rules to program the shape and symmetry of organic cage cavities by designing edge pieces that bias the orientation of the amide linkages. We apply the rules to synthesise cages with well-defined cavities, supported by evidence from crystallography, spectroscopy and modelling. Access to low-symmetry, self-assembled organic cages such as those presented, will widen the current bottleneck preventing study of organic enzyme mimics, and provide synthetic tools for novel functional material design.

Water-Soluble Molecular Cages for Biological Applications

The field of molecular cages has attracted increasing interest in relation to the development of biological applications, as evidenced by the remarkable examples published in recent years. Two key factors have contributed to this achievement: First, the remarkable and adjustable host-guest chemical properties of molecular cages make them highly suitable for biological applications. This allows encapsulating therapeutic molecules to improve their properties. Second, significant advances have been made in synthetic methods to create water-soluble molecular cages. Achieving the necessary water solubility is a significant challenge, which in most cases requires specific chemical groups to overcome the inherent hydrophobic nature of the molecular cages which feature the organic components of the cage. This can be achieved by either incorporating water-solubilizing groups with negative/positive charges, polyethylene glycol chains, etc.; or by introducing charges directly into the cage structure itself. These synthetic strategies allow preparing water-soluble molecular cages for diverse biological applications, including cages' anticancer activity, anticancer drug delivery, photodynamic therapy, and molecular recognition of biological molecules. In the review we describe selected examples that show the main concepts to achieve water solubility in molecular cages and some selected recent biological applications.

Chiral Covalent Organic Cages: Structural Isomerism and Enantioselective Catalysis

Covalent organic cages are a prominent class of discrete porous architectures; however, their structural isomerism remains relatively unexplored. Here, we demonstrate the structural isomerism of chiral covalent organic cages that renders distinct enantioselective catalytic properties. Imine condensations of tetra-topic 5,10-di(3,5-diformylphenyl)-5,10-dihydrophenazine and ditopic 1,2-cyclohexanediamine produce two chiral [4 + 8] organic cage isomers with totally different topologies and geometries that depend on the orientations of four tetraaldehyde units with respect to each other. One isomer (PN-1) has an unprecedented Johnson-type J26 structure, whereas another (PN-2) adopts a tetragonal prismatic structure. After the reduction of the imine linkages, the cages are transformed into two amine bond-linked isomers PN-1R and PN-2R. After binding to Ni(II) ions, both can serve as efficient catalysts for asymmetric Michael additions, whereas PN-2R affords obviously higher enantioselectivity and reactivity than PN-1R presumably because of its large cavity and open windows that can concentrate reactants for the reactions. Density-functional theory (DFT) calculations further confirm that the enantioselective catalytic performance varies depending on the isomer.

Design and assembly of porous organic cages

Porous organic cages (POCs) represent a notable category of porous materials, showing remarkable material properties due to their inherent porosity. Unlike extended frameworks which are constructed by strong covalent or coordination bonds, POCs are composed of discrete molecular units held together by weak intermolecular forces. Their structure and chemical traits can be systematically tailored, making them suitable for a range of applications including gas storage and separation, molecular separation and recognition, catalysis, and proton and ion conduction. This review provides a comprehensive overview of POCs, covering their synthesis methods, structure and properties, computational approaches, and applications, serving as a primer for those who are new to the domain. A special emphasis is placed on the growing role of computational methods, highlighting how advanced data-driven techniques and automation are increasingly aiding the rapid exploration and understanding of POCs. We conclude by addressing the prevailing challenges and future prospects in the field.

Deep generative design of porous organic cages via a variational autoencoder

Porous organic cages (POCs) are a class of porous molecular materials characterised by their tunable, intrinsic porosity; this functional property makes them candidates for applications including guest storage and separation. Typically formed via dynamic covalent chemistry reactions from multifunctionalised molecular precursors, there is an enormous potential chemical space for POCs due to the fact they can be formed by combining two relatively small organic molecules, which themselves have an enormous chemical space. However, identifying suitable molecular precursors for POC formation is challenging, as POCs often lack shape persistence (the cage collapses upon solvent removal with loss of its cavity), thus losing a key functional property (porosity). Generative machine learning models have potential for targeted computational design of large functional molecular systems such as POCs. Here, we present a deep-learning-enabled generative model, Cage-VAE, for the targeted generation of shape-persistent POCs. We demonstrate the capacity of Cage-VAE to propose novel, shape-persistent POCs, via integration with multiple efficient sampling methods, including Bayesian optimisation and spherical linear interpolation.

Conformational control enables boroxine-to-boronate cage metamorphosis

The discovery of molecular organic cages (MOCs) is inhibited by the limited organic-chemical space of the building blocks designed to fulfill strict geometric requirements for efficient assembly. Using intramolecular attractive or repulsive non-covalent interactions to control the conformation of flexible systems can effectively augment the variety of building blocks, ultimately facilitating the exploration of new MOCs. In this study, we introduce a set of boronic acid tripods that were designed using rational design principles. Conformational control was induced by extending the tripod's arms by a 2,3-dimethylbenzene unit, leading to the efficient formation of a tetrapodal nanometer-sized boroxine cage. The new building block's versatility was demonstrated by performing cage metamorphosis upon adding an aromatic tetraol. This led to a quantitative boroxine-to-boronate transformation and a topological shift from tetrahedral to trigonal bipyramidal.

Systematic exploration of accessible topologies of cage molecules via minimalistic models

Cages are macrocyclic structures with an intrinsic internal cavity that support applications in separations, sensing and catalysis. These materials can be synthesised via self-assembly of organic or metal-organic building blocks. Their bottom-up synthesis and the diversity in building block chemistry allows for fine-tuning of their shape and properties towards a target property. However, it is not straightforward to predict the outcome of self-assembly, and, thus, the structures that are practically accessible during synthesis. Indeed, such a prediction becomes more difficult as problems related to the flexibility of the building blocks or increased combinatorics lead to a higher level of complexity and increased computational costs. Molecular models, and their coarse-graining into simplified representations, may be very useful to this end. Here, we develop a minimalistic toy model of cage-like molecules to explore the stable space of different cage topologies based on a few fundamental geometric building block parameters. Our results capture, despite the simplifications of the model, known geometrical design rules in synthetic cage molecules and uncover the role of building block coordination number and flexibility on the stability of cage topologies. This leads to a large-scale and systematic exploration of design principles, generating data that we expect could be analysed through expandable approaches towards the rational design of self-assembled porous architectures.

Porous Organic Cages

Porous organic cages (POCs) are a relatively new class of low-density crystalline materials that have emerged as a versatile platform for investigating molecular recognition, gas storage and separation, and proton conduction, with potential applications in the fields of porous liquids, highly permeable membranes, heterogeneous catalysis, and microreactors. In common with highly extended porous structures, such as metal-organic frameworks (MOFs), covalent organic frameworks (COFs), and porous organic polymers (POPs), POCs possess all of the advantages of highly specific surface areas, porosities, open pore channels, and tunable structures. In addition, they have discrete molecular structures and exhibit good to excellent solubilities in common solvents, enabling their solution dispersibility and processability─properties that are not readily available in the case of the well-established, insoluble, extended porous frameworks. Here, we present a critical review summarizing in detail recent progress and breakthroughs─especially during the past five years─of all the POCs while taking a close look at their strategic design, precise synthesis, including both irreversible bond-forming chemistry and dynamic covalent chemistry, advanced characterization, and diverse applications. We highlight representative POC examples in an attempt to gain some understanding of their structure-function relationships. We also discuss future challenges and opportunities in the design, synthesis, characterization, and application of POCs. We anticipate that this review will be useful to researchers working in this field when it comes to designing and developing new POCs with desired functions.

Importance of Precursor Adaptability in the Assembly of Molecular Organic Cages

For molecular architectures based on dynamic covalent chemistry (DCvC), strict preorganization is a paradigmatic concept and the generally accepted strategy for their rational design. This results in the creation of highly rigid building blocks which are expected to fulfill the ideal geometry of the assembly, coming at a price that small geometric mismatches result in unpredicted and/or unproductive reaction outcomes. In this study, we show that feet of a tripodal platform have a great influence on the assembly of tetrahedral organic cages based on boronate ester formation. The aryl benzyl ether-functionalized building blocks perform significantly better than their alkyl-functionalized equivalents. Experimentally and using density functional theory geometry optimization of the cage structures, we prove that unexpectedly, this is not due to solubility but because of the enhanced capability of the aryl benzyl ether-functionalized building blocks to fit the ideal geometry of the assembly. This introduces the concept of building block adaptability to overcome geometrical mismatches in DCvC systems.

Pore topology analysis in porous molecular systems

Porous molecular materials are constructed from molecules that assemble in the solid-state such that there are cavities or an interconnected pore network. It is challenging to control the assembly of these systems, as the interactions between the molecules are generally weak, and subtle changes in the molecular structure can lead to vastly different intermolecular interactions and subsequently different crystal packing arrangements. Similarly, the use of different solvents for crystallization, or the introduction of solvent vapour, can result in different polymorphs and pore networks being formed. It is difficult to uniquely describe the pore networks formed, and thus we analyse 1033 crystal structures of porous molecular systems to determine the underlying topology of their void spaces and potential guest diffusion networks. Material-agnostic topology definitions are applied. We use the underlying topological nets to examine whether it is possible to apply isoreticular design principles to porous molecular materials. Overall, our automatic analysis of a large dataset gives a general insight into the relationships between molecular topologies and the topological nets of their pore network. We show that while porous molecular systems tend to pack similarly to non-porous molecules, the topologies of their pore distributions resemble those of more prominent porous materials, such as metal-organic frameworks and covalent organic frameworks.

An Organic Molecular Nanobarrel that Hosts and Solubilizes C60

Organic cages have gained increasing attention in recent years as molecular hosts and porous materials. Among these, barrel-shaped cages or molecular nanobarrels are promising systems to encapsulate large hosts as they possess windows of the same size as their internal cavity. However, these systems have received little attention and remain practically unexplored despite their potential. Herein, we report the design and synthesis of a new trigonal prismatic organic nanobarrel with two large triangular windows with a diameter of 12.7 Å optimal for the encapsulation of C₆₀ . Remarkably, this organic nanobarrel shows a high affinity for C₆₀ in solvents in which C₆₀ is virtually insoluble, providing stable solutions of C₆₀ .

Computational modeling to assist in the discovery of supramolecular materials

Computational modeling is increasingly used to assist in the discovery of supramolecular materials. Supramolecular materials are typically primarily built from organic components that are self-assembled through noncovalent bonding and have potential applications, including in selective binding, sorption, molecular separations, catalysis, optoelectronics, sensing, and as molecular machines. In this review, the key areas where computational prediction can assist in the discovery of supramolecular materials, including in structure prediction, property prediction, and the prediction of how to synthesize a hypothetical material are discussed, before exploring the potential impact of artificial intelligence techniques on the field. Throughout, the importance of close integration with experimental materials discovery programs will be highlighted. A series of case studies from the author's work across some different supramolecular material classes will be discussed, before finishing with a discussion of the outlook for the field.

Photoresponsive Dissipative Macrocycles Using Visible-Light-Switchable Azobenzenes

Visible light can be used to shift dynamic covalent imine assemblies out of equilibrium. We studied a fluorinated azobenzene building block that reliably undergoes geometric isomerism upon irradiation. The building block was used in combination with two different amines, ethylenediamine and R,R-1,2-diaminocyclohexane, to create a library of imine macrocycles. Whereas the simple amine can be used to access a polymeric state and a defined bowl-shaped macrocycle, the chiral amine gives access to a rich network of macrocycles that undergo both isomerisation as well as interconversion between different macrocyclic species, thereby allowing for control over the number of monomers involved in the cyclo-oligomerization; ¹ H- and ¹⁹ F-DOSY NMR, MALDI-MS measurements, and UV/Vis spectroscopy were used to study the processes.

Identifying porous cage subsets in the Cambridge Structural Database using topological data analysis

As rationally designable materials, the variety and number of synthesised metal-organic cages (MOCs) and organic cages (OCs) are expected to grow in the Cambridge Structural Database (CSD). In this regard, two of the most important questions are, which structures are already present in the CSD and how can they be identified? Here, we present a cage mining methodology based on topological data analysis and a combination of supervised and unsupervised learning that led to the derivation of - to the best of our knowledge - the first and only MOC dataset of 1839 structures and the largest experimental OC dataset of 7736 cages, as of March 2022. We illustrate the use of such datasets with a high-throughput screening of MOCs and OCs for xenon/krypton separation, important gases in multiple industries, including healthcare.

Coarse-grained modelling to predict the packing of porous organic cages

How molecules pack has vital ramifications for their applications as functional molecular materials. Small changes in a molecule's functionality can lead to large, non-intuitive, changes in their global solid-state packing, resulting in difficulty in targeted design. Predicting the crystal structure of organic molecules from only their molecular structure is a well-known problem plaguing crystal engineering. Although relevant to the properties of many organic molecules, the packing behaviour of modular porous materials, such as porous organic cages (POCs), greatly impacts the properties of the material. We present a novel way of predicting the solid-state phase behaviour of POCs by using a simplistic model containing the dominant degrees of freedom driving crystalline phase formation. We employ coarse-grained simulations to systematically study how chemical functionality of pseudo-octahedral cages can be used to manipulate the solid-state phase formation of POCs. Our results support those of experimentally reported structures, showing that for cages which pack via their windows forming a porous network, only one phase is formed, whereas when cages pack via their windows and arenes, the phase behaviour is more complex. While presenting a lower computational cost route for predicting molecular crystal packing, coarse-grained models also allow for the development of design rules which we start to formulate through our results.

Purely Covalent Molecular Cages and Containers for Guest Encapsulation

Cage compounds offer unique binding pockets similar to enzyme-binding sites, which can be customized in terms of size, shape, and functional groups to point toward the cavity and many other parameters. Different synthetic strategies have been developed to create a toolkit of methods that allow preparing tailor-made organic cages for a number of distinct applications, such as gas separation, molecular recognition, molecular encapsulation, hosts for catalysis, etc. These examples show the versatility and high selectivity that can be achieved using cages, which is impossible by employing other molecular systems. This review explores the progress made in the field of fully organic molecular cages and containers by focusing on the properties of the cavity and their application to encapsulate guests.

Two Mixed Alkali-Metal Borates Templated from Cations to Clusters

Two mixed alkali-metal borates, K_0.5Li[B₆O₁₀]·0.5H₃O (1) and [(μ₅-OH)@(Na₄Li)]_0.5[B₆O₁₀]·0.5B(OH)₃ (2), have been prepared under solvothermal conditions. Both of them are obtained in the same synthetic system and contain a B₆O₁₃^8- cluster as the structural-building unit (SBU) but exhibit quite different structural features. 1 is a centric three-dimensional (3D) oxoboron (B-O) framework, where templated mixed K⁺ and Li⁺ cations occupied the cavities of the structure. 2 crystallizes in an acentric space group under the templating effect of a unique acentric alkali-metal cluster [(μ₅-OH)@(Na₄Li)]⁴⁺. The SBU of 2 is also the B₆O₁₃^8- cluster, which acts as six-connected nodes linked together to form a 3D B-O framework, showing different characters from 1 because of two types of templates; the acentric [(μ₅-OH)@(Na₄Li)]⁴⁺ clusters and the electroneutral B (OH)₃ groups fill in two different cages in the framework and further connect each other via Na-O-B bonds to build a novel two-dimensional (2D) wavy bricklike network, resulting in a 3D B-O framework interpenetrated by a 2D [(μ₅-OH)@(Na₄Li)]-B(OH)₃ network. As a crystal material with an acentric space group, 2 shows a good second harmonic generation response of about 2.8 times that of KDP (KH₂PO₄) and has a cutoff edge below 190 nm, which suggests that 2 is a potential deep-UV NLO material.

Recent trends in organic cage synthesis: push towards water-soluble organic cages

Research on organic cages has blossomed over the past few years into a mature field of study which can contribute to solving some of the challenging problems. In this review we aim to showcase the recent trends in synthesis of organic cages including a brief discussion on their use in catalysis, gas sorption, host-guest chemistry and energy transfer. Among the organic cages, water-soluble analogues are a special class of compounds which have gained renewed attention in recent times. Due to their advantage of being compatible with water, such cages have the potential of showing biomimetic activities and can find use in drug delivery and also as hosts for catalysis in aqueous medium. Hence, the synthetic strategies for the formation of water-soluble organic cages shall be discussed along with their potential applications.

Supramolecular networks by imine halogen bonding

Halogen bonding of neutral donors using imine groups of porous organic cage compounds as acceptors leads to the formation of halogen-bonded frameworks. We report the use of two different imine cages, in combination with three electron-poor halogen bond donors. Four resulting solid-state structures elucidated by single-crystal X-ray analysis are presented and analysed for the first time by plane-wave DFT calculations and QTAIM-analyses of the entire unit cells, demonstrating the formation of halogen bonds within the networks. The supramolecular frameworks can be obtained either from solution or mechanochemically by liquid-assisted grinding.

Rigidity and Flexibility in Rotaxanes and Their Relatives; On Being Stubborn and Easy-Going

Rotaxanes are an emerging class of molecules composed of two building blocks: macrocycles and threads. Rotaxanes, and their pseudorotaxane and polyrotaxane relatives, serve as prototypes for molecular-level switches and machines and as components in materials like elastic polymers and 3D printing inks. The rigidity and flexibility of these molecules is a characteristic feature of their design. However, the mechanical properties of the assembled rotaxane and its components are rarely examined directly, and the translation of these properties from molecules to bulk materials is understudied. In this Review, we consider the mechanical properties of rotaxanes by making use of concepts borrowed from physical organic chemistry. Rigid molecules have fewer accessible conformations with higher energy barriers while flexible molecules have more accessible conformations and lower energy barriers. The macrocycles and threads become rigidified when threaded together as rotaxanes in which the formation of intermolecular interactions and increased steric contacts collectively reduce the conformational space and raise barriers. Conversely, rotational and translational isomerism in rotaxanes adds novel modes of flexibility. We find that rigidification in rotaxanes is almost universal, but novel degrees of flexibility can be introduced. Both have roles to play in the function of rotaxanes.

Fast, solvent-free synthesis of Ferrocene-containing Organic Cages via dynamic covalent chemistry in the solid state

A simple, solvent-free synthetic protocol towards the synthesis of organic self-assembled macromolecules has been established. By employing mechanochemistry using glassware readily available to every organic chemist, we were able to...

Explainable graph neural networks for organic cages

The development of accurate and explicable machine learning models to predict the properties of topologically complex systems is a challenge in materials science. Porous organic cages, a class of polycyclic molecular materials, have potential application in molecular separations, catalysis and encapsulation. For most applications of porous organic cages, having a permanent internal cavity in the absence of solvent, a property termed “shape persistence” is critical. Here, we report the development of Graph Neural Networks (GNNs) to predict the shape persistence of organic cages. Graph neural networks are a class of neural networks where the data, in our case that of organic cages, are represented by graphs. The performance of the GNN models was measured against a previously reported computational database of organic cages formed through a range of [4 + 6] reactions with a variety of reaction chemistries. The reported GNNs have an improved prediction accuracy and transferability compared to random forest predictions. Apart from the improvement in predictive power, we explored the explicability of the GNNs by computing the integrated gradient of the GNN input. The contribution of monomers and molecular fragments to the shape persistence of the organic cages could be quantitatively evaluated with integrated gradients. With the added explicability of the GNNs, it was possible not only to accurately predict the property of organic materials, but also to interpret the predictions of the deep learning models and provide structural insights for the discovery of future materials.

We report the development of explainable Graph Neural Networks to predict shape persistence of organic cages. Integrated gradient analysis identifies collapse-inducing molecular fragments and helps chemists design more shape persistent structures.

Materials Precursor Score: Modeling Chemists' Intuition for the Synthetic Accessibility of Porous Organic Cage Precursors

Computation is increasingly being used to try to accelerate the discovery of new materials. One specific example of this is porous molecular materials, specifically porous organic cages, where the porosity of the materials predominantly comes from the internal cavities of the molecules themselves. The computational discovery of novel structures with useful properties is currently hindered by the difficulty in transitioning from a computational prediction to synthetic realization. Attempts at experimental validation are often time-consuming, expensive, and frequently, the key bottleneck of material discovery. In this work, we developed a computational screening workflow for porous molecules that includes consideration of the synthetic difficulty of material precursors, aimed at easing the transition between computational prediction and experimental realization. We trained a machine learning model by first collecting data on 12,553 molecules categorized either as "easy-to-synthesize" or "difficult-to-synthesize" by expert chemists with years of experience in organic synthesis. We used an approach to address the class imbalance present in our data set, producing a binary classifier able to categorize easy-to-synthesize molecules with few false positives. We then used our model during computational screening for porous organic molecules to bias toward precursors whose easier synthesis requirements would make them promising candidates for experimental realization and material development. We found that even by limiting precursors to those that are easier-to-synthesize, we are still able to identify cages with favorable, and even some rare, properties.

Cucurbitimines - imine cages with concave walls

The variety of shape-persistent organic cages by imine bond formation has tremendously enlarged in recent years by using different building blocks (aldehydes and amines) in the condensation reactions. Here, we describe the use of a kinked tetraldehyde to generate pumpkin-shaped cages with concave walls, similar to cucurbiturils.

Kinked tetraaldehyde building blocks lead in condensation reactions with diamines to pumpkin shaped cages – the cucurbitimines.

stk: An extendable Python framework for automated molecular and supramolecular structure assembly and discovery

Computational software workflows are emerging as all-in-one solutions to speed up the discovery of new materials. Many computational approaches require the generation of realistic structural models for property prediction and candidate screening. However, molecular and supramolecular materials represent classes of materials with many potential applications for which there is no go-to database of existing structures or general protocol for generating structures. Here, we report a new version of the supramolecular toolkit, stk, an open-source, extendable, and modular Python framework for general structure generation of (supra)molecular structures. Our construction approach works on arbitrary building blocks and topologies and minimizes the input required from the user, making stk user-friendly and applicable to many material classes. This version of stk includes metal-containing structures and rotaxanes as well as general implementation and interface improvements. Additionally, this version includes built-in tools for exploring chemical space with an evolutionary algorithm and tools for database generation and visualization. The latest version of stk is freely available at github.com/lukasturcani/stk.

Supramolecular Alloys from Fluorinated Hybrid Tri4 Di6 Imine Cages

To create innovative materials, efficient control and engineering of pore sizes and their characteristics, crystallinity and stability is required. Eight hybrid Tri4 Di6 imine cages with a tunable degree of fluorination and one fully fluorinated Tri4 Di6 imine cage are investigated. Although the fluorinated and the non-fluorinated building blocks used herein differ vastly in reactivity, it was possible to gain control over the outcome of the self-assembly process, by carefully controlling the feed ratio. This represents the first hybrid material based on fluorinated/hydrogenated porous organic cages (POCs). These cages with unlimited miscibility in the solid state were obtained as highly crystalline samples after recrystallization and even showed retention of the crystal lattice, forming alloys. All mixtures and the fully fluorinated Tri4 Di6 imine cage were analyzed by MALDI-MS, single-crystal XRD, powder XRD and in regard to thermal stability (TGA).

Chiral Self-sorting of Giant Cubic [8+12] Salicylimine Cage Compounds

Chiral self-sorting is intricately connected to the complicated chiral processes observed in nature and no artificial systems of comparably complexity have been generated by chemists. However, only a few examples of purely organic molecules have been reported so far, where the self-sorting process could be controlled. Herein, we describe the chiral self-sorting of large cubic [8+12] salicylimine cage compounds based on a chiral TBTQ precursor. Out of 23 possible cage isomers only the enantiopure and a meso cage were observed to be formed, which have been unambiguously characterized by single crystal X-ray diffraction. Furthermore, by careful choice of solvent the formation of meso cage could be controlled. With internal diameters of din =3.3-3.5 nm these cages are among the largest organic cage compounds characterized and show very high specific surface areas up to approx. 1500 m2  g-1 after desolvation.

Integrating Computational and Experimental Workflows for Accelerated Organic Materials Discovery

Organic materials find application in a range of areas, including optoelectronics, sensing, encapsulation, molecular separations, and photocatalysis. The discovery of materials is frustratingly slow however, particularly when contrasted to the vast chemical space of possibilities based on the near limitless options for organic molecular precursors. The difficulty in predicting the material assembly, and consequent properties, of any molecule is another significant roadblock to targeted materials design. There has been significant progress in the development of computational approaches to screen large numbers of materials, for both their structure and properties, helping guide synthetic researchers toward promising materials. In particular, artificial intelligence techniques have the potential to make significant impact in many elements of the discovery process. Alongside this, automation and robotics are increasing the scale and speed with which materials synthesis can be realized. Herein, the focus is on demonstrating the power of integrating computational and experimental materials discovery programmes, including both a summary of key situations where approaches can be combined and a series of case studies that demonstrate recent successes.

A Giant [8+12] Boronic Ester Cage with 48 Terminal Alkene Units in the Periphery for Postsynthetic Alkene Metathesis

Dynamic covalent chemistry (DCC) is a powerful synthetic tool to construct large defined molecules in one step from rather simple precursors. The advantage of the intrinsic dynamics of the applied reversible reaction steps is a self‐correction under the chosen conditions, to achieve high yields of the target compound. To date, only a few examples are known, in which DCC was used to build up a molecular defined but larger product that was chemically transferred to a more stable congener in a second (irreversible) step. Here, we present a nanometer‐sized [8+12] boronic ester cage containing 48 peripheral terminal alkene units which allows to put a hydrocarbon exoskeleton around the cage via alkene metathesis.

Can we predict materials that can be synthesised?

The discovery of materials is an important element in the development of new technologies and abilities that can help humanity tackle many challenges. Materials discovery is frustratingly slow, with the large time and resource cost often providing only small gains in property performance. Furthermore, researchers are unwilling to take large risks that they will only know the outcome of months or years later. Computation is playing an increasing role in allowing rapid screening of large numbers of materials from vast search space to identify promising candidates for laboratory synthesis and testing. However, there is a problem, in that many materials computationally predicted to have encouraging properties cannot be readily realised in the lab. This minireview looks at how we can tackle the problem of confirming that hypothetical materials are synthetically realisable, through consideration of all the stages of the materials discovery process, from obtaining the components, reacting them to a material in the correct structure, through to processing into a desired form. In an ideal world, a material prediction would come with an associated 'recipe' for the successful laboratory preparation of the material. We discuss the opportunity to thus prevent wasted effort in experimental discovery programmes, including those using automation, to accelerate the discovery of novel materials.

A Robust Porous Quinoline Cage: Transformation of a [4+6] Salicylimine Cage by Povarov Cyclization

Porous shape-persistent organic cages have become the object of interest in recent years because they are soluble and thus processable from solution. A variety of cages can be achieved by applying dynamic covalent chemistry (DCC), but they are less chemically stable. Here the transformation of a salicylimine cage into a quinoline cage by a twelve-fold Povarov reaction as the key step is described. Besides the chemical stability of the cage over a broad pH regime, it shows a unique absorption and emission depending on acid concentration. Furthermore, thin films for the vapor detection of acids were investigated, showing color switches from pale-yellow to red, and characteristic emission profiles.

Examination of the Dynamic Covalent Chemistry of [2 + 3]-Imine Cages

The synthesis of shape-persistent organic cage compounds by the formation of imine bonds opens the possibility to realize cages of different sizes, geometries, topologies, and functions. It is generally assumed that the imine bond is rather chemically labile allowing a self-correction mechanism until thermodynamic equilibrium is reached, which is often the case if a cage is formed. However, there are some contradictory experimental data to this assumption. To get a deeper insight into the imine bond dynamics of covalent organic cages, we studied the formation and exchange of both dialdehydes and triamines of two different [2 + 3] imine cages with the aid of a deuterated dialdehyde molecular building block.

Reticular Chemistry in the Construction of Porous Organic Cages

Reticular chemistry offers the possibility of systematic design of porous materials with different pores by varying the building blocks, while the emerging porous organic cage (POC) system remains generally unexplored. A series of new POCs with dimeric cages with odd-even behaviors, unprecedented trimeric triangular prisms, and the largest recorded hexameric octahedra have been prepared. These POCs are all constructed from the same tetratopic tetraformylresorcin[4]arene cavitand by simply varying the diamine ligands through Schiff-base reactions and are fully characterized by X-ray crystallography, gas sorption measurements, NMR spectroscopy, and mass spectrometry. The odd-even effects in the POC conformation changes of the [2 + 4] dimeric cages have been confirmed by density functional theory calculations, which are the first examples of odd-even effects reported in the cavitand-based cage system. Moreover, the "V" shape phenylenediamine linkers are responsible for the novel [3 + 6] triangular prisms. The window size and environment can be easily functionalized by different groups, providing a promising platform for the construction of multivariate POCs. Use of linear phenylenediamines led to record-breakingly large [6 + 12] truncated octahedral cages, the maximum inner cavity diameters and volumes of which could be readily modulated by increasing the spacer length of the phenylenediamine linkers. This work can lead to an understanding of the self-assembly behaviors of POCs and also sheds light on the rational design of POC materials for practical applications.

3D Cage COFs: A Dynamic Three-Dimensional Covalent Organic Framework with High-Connectivity Organic Cage Nodes

Three-dimensional (3D) covalent organic frameworks (COFs) are rare because there is a limited choice of organic building blocks that offer multiple reactive sites in a polyhedral geometry. Here, we synthesized an organic cage molecule (Cage-6-NH₂) that was used as a triangular prism node to yield the first cage-based 3D COF, 3D-CageCOF-1. This COF adopts an unreported 2-fold interpenetrated acs topology and exhibits reversible dynamic behavior, switching between a small-pore (sp) structure and a large-pore (lp) structure. It also shows high CO₂ uptake and captures water at low humidity (<40%). This demonstrates the potential for expanding the structural complexity of 3D COFs by using organic cages as the building units.

Inducing Social Self-Sorting in Organic Cages To Tune The Shape of The Internal Cavity

Many interesting target guest molecules have low symmetry, yet most methods for synthesising hosts result in highly symmetrical capsules. Methods of generating lower symmetry pores are thus required to maximise the binding affinity in host-guest complexes. Herein, we use mixtures of tetraaldehyde building blocks with cyclohexanediamine to access low-symmetry imine cages. Whether a low-energy cage is isolated can be correctly predicted from the thermodynamic preference observed in computational models. The stability of the observed structures depends on the geometrical match of the aldehyde building blocks. One bent aldehyde stands out as unable to assemble into high-symmetry cages-and the same aldehyde generates low-symmetry socially self-sorted cages when combined with a linear aldehyde. We exploit this finding to synthesise a family of low-symmetry cages containing heteroatoms, illustrating that pores of varying geometries and surface chemistries may be reliably accessed through computational prediction and self-sorting.

High-Throughput Approaches for the Discovery of Supramolecular Organic Cages

The assembly of complex molecules, such as organic cages, can be achieved through supramolecular and dynamic covalent strategies. Their use in a range of applications has been demonstrated, including gas uptake, molecular separations, and in catalysis. However, the targeted design and synthesis of new species for particular applications is challenging, particularly as the systems become more complex. High-throughput computation-only and experiment-only approaches have been developed to streamline the discovery process, although are still not widely implemented. Additionally, combined hybrid workflows can dramatically accelerate the discovery process and lead to the serendipitous discovery and rationalisation of new supramolecular assemblies that would not have been designed based on intuition alone. This Minireview focuses on the advances in high-throughput approaches that have been developed and applied in the discovery of supramolecular organic cages.

Modulation of hydrophilicity inside the cavity of molecular rectangles self-assembled under ambient conditions

Three novel molecular rectangles of tetranuclear Cu(ii) with a variation in the flexible methylene chain length on the two opposite sides have been synthesized from a one pot self-assembly reaction in a methanol-water mixture under ambient conditions. Their solid state molecular structures are determined by single crystal X-ray diffractometry, while their structural integrity in the solution state is confirmed by electrospray mass spectrometry. With such a subtle variation, modulation of hydrophilicity inside the cavity of these molecular rectangles is achieved as evident from the water and methanol vapor sorption studies.

Geometric landscapes for material discovery within energy-structure-function maps

Porous molecular crystals are an emerging class of porous materials formed by crystallisation of molecules with weak intermolecular interactions, which distinguishes them from extended nanoporous materials like metal-organic frameworks (MOFs). To aid discovery of porous molecular crystals for desired applications, energy-structure-function (ESF) maps were developed that combine a priori prediction of both the crystal structure and its functional properties. However, it is a challenge to represent the high-dimensional structural and functional landscapes of an ESF map and to identify energetically favourable and functionally interesting polymorphs among the 1000s to 10 000s of structures typically on a single ESF map. Here, we introduce geometric landscapes, a representation for ESF maps based on geometric similarity, quantified by persistent homology. We show that this representation allows the exploration of complex ESF maps, automatically pinpointing interesting crystalline phases available to the molecule. Furthermore, we show that geometric landscapes can serve as an accountable descriptor for porous materials to predict their performance for gas adsorption applications. A machine learning model trained using this geometric similarity could reach a remarkable accuracy in predicting the materials' performance for methane storage applications.

A Robotics-Inspired Screening Algorithm for Molecular Caging Prediction

We define a molecular caging complex as a pair of molecules in which one molecule (the “host” or “cage”) possesses a cavity that can encapsulate the other molecule (the “guest”) and prevent it from escaping. Molecular caging complexes can be useful in applications such as molecular shape sorting, drug delivery, and molecular immobilization in materials science, to name just a few. However, the design and computational discovery of new caging complexes is a challenging task, as it is hard to predict whether one molecule can encapsulate another because their shapes can be quite complex. In this paper, we propose a computational screening method that predicts whether a given pair of molecules form a caging complex. Our method is based on a caging verification algorithm that was designed by our group for applications in robotic manipulation. We tested our algorithm on three pairs of molecules that were previously described in a pioneering work on molecular caging complexes and found that our results are fully consistent with the previously reported ones. Furthermore, we performed a screening experiment on a data set consisting of 46 hosts and four guests and used our algorithm to predict which pairs are likely to form caging complexes. Our method is computationally efficient and can be integrated into a screening pipeline to complement experimental techniques.