Computationally-inspired discovery of an unsymmetrical porous organic cage

Exploration of the polymorphic solid-state landscape of an amide-linked organic cage using computation and automation

Organic cages can possess complex, functionalised cavities that make them promising candidates for synthetic enzyme mimics. Conformationally flexible, chemically robust structures are needed for adaptable guest binding and catalysis, but rapidly exchanging systems are difficult to resolve in solution. Here, we use low-cost calculations and high-throughput crystallisation to identify accessible conformers of a recently reported organic cage by 'locking' them in the solid state. The conformers exhibit varying distances between the internal carboxylic acid groups, suggesting adaptability for binding a wide array of target guest molecules.

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.

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.

Squarate-Tethered Enantiomeric Imido-Pd(II) Cages for Recognition and Separation of Chiral Organic Molecules

Chiral coordination cages have emerged as an efficient platform for enantioselective processes via host-guest interactions. Here, we report an enantiomeric pair of tetrahedral cages of formula [(Pd3[PO(N(*CH(CH3)Ph)3])4(C4O4)6] supported by chiral tris(imido)phosphate trianions and squarate (C4O4)2- linkers. These cages exhibit unusual coordination isomerism for Pd(II)-linker bonds compared with the other Pd(II) cages of this family. Further, they were employed for the recognition and separation of small chiral molecules containing various functionalities. High enantioselectivities of 67 and 41 were found in the case of R-4-hydroxydihydrofuran-2(3H)-one and S-epichlorohydrin, recognized by the R-isomer of the cage. Chiral separation studies showed remarkable enantiomeric excess values of 93 and 85% for S-epichlorohydrin and R-4-benzyl-2-oxazolidinone, respectively, from their racemic mixtures. These studies showcase the potential of coordination cages for enantioselective applications.

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.

A porphyrin-based molecular cage guided by designed local-electric field is highly selective and efficient

The present work outlines a general methodology for designing efficient catalytic machineries that can easily be tweaked to meet the demands of the target reactions. This work utilizes a principle of the designed local electric field (LEF) as the driver for an efficient catalyst. It is demonstrated that by tweaking the LEF, we can catalyze the desired hydroxylation products with enantioselectivity that can be changed at will. Using computation tools, we caged a synthetic analog of heme porphyrin (HM1) and investigated the pharmaceutically relevant conversion of tetralin to tetralol, inside the modified supramolecular cage. The QM/MM calculations demonstrate a resulting catalytic efficiency with virtually absolute R-selectivity for the tetralin hydroxylation. Our calculations show that the LEF of the supramolecular cage and HM1 exert a strong electric field along the Fe-O reaction axis, which is the main driving force for enhanced reactivity. At the same time, the supramolecular cage applies a lateral LEF that regulates the enantioselectivity. We further demonstrate that swapping the charged/polar substitution in the supramolecular cage switches the lateral LEF which changes the enantioselectivity of hydroxylation from R to S.

Supramolecular Enhancement of Electrochemical Nitrate Reduction Catalyzed by Cobalt Porphyrin Organic Cages for Ammonia Electrosynthesis in Water

The electrochemical nitrate (NO3 - ) reduction reaction (NO3 RR) to ammonia (NH3 ) represents a sustainable approach for denitrification to balance global nitrogen cycles and an alternative to traditional thermal Haber-Bosch processes. Here, we present a supramolecular strategy for promoting NH3 production in water from NO3 RR by integrating two-dimensional (2D) molecular cobalt porphyrin (CoTPP) units into a three-dimensional (3D) porous organic cage architecture. The porphyrin box CoPB-C8 enhances electrochemical active site exposure, facilitates substrate-catalyst interactions, and improves catalyst stability, leading to turnover numbers and frequencies for NH3 production exceeding 200,000 and 56 s-1 , respectively. These values represent a 15-fold increase in NO3 RR activity and 200-mV improvement in overpotential for the 3D CoPB-C8 box structure compared to its 2D CoTPP counterpart. Synthetic tuning of peripheral alkyl substituents highlights the importance of supramolecular porosity and cavity size on electrochemical NO3 RR activity. These findings establish the incorporation of 2D molecular units into 3D confined space microenvironments as an effective supramolecular design strategy for enhancing electrocatalysis.

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.

Beyond Platonic: How to Build Metal-Organic Polyhedra Capable of Binding Low-Symmetry, Information-Rich Molecular Cargoes

The field of metallosupramolecular chemistry has advanced rapidly in recent years. Much work in this area has focused on the formation of hollow self-assembled metal-organic architectures and exploration of the applications of their confined nanospaces. These discrete, soluble structures incorporate metal ions as 'glue' to link organic ligands together into polyhedra.Most of the architectures employed thus far have been highly symmetrical, as these have been the easiest to prepare. Such high-symmetry structures contain pseudospherical cavities, and so typically bind roughly spherical guests. Biomolecules and high-value synthetic compounds are rarely isotropic, highly-symmetrical species. To bind, sense, separate, and transform such substrates, new, lower-symmetry, metal-organic cages are needed. Herein we summarize recent approaches, which taken together form the first draft of a handbook for the design of higher-complexity, lower-symmetry, self-assembled metal-organic architectures.

Enabling Technology for Supramolecular Chemistry

Supramolecular materials-materials that exploit non-covalent interactions-are increasing in structural complexity, selectivity, function, stability, and scalability, but their use in applications has been comparatively limited. In this Minireview, we summarize the opportunities presented by enabling technology-flow chemistry, high-throughput screening, and automation-to wield greater control over the processes in supramolecular chemistry and accelerate the discovery and use of self-assembled systems. Finally, we give an outlook for how these tools could transform the future of the field.

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.

High-Throughput Computational Evaluation of Low Symmetry Pd2 L4 Cages to Aid in System Design*

Unsymmetrical ditopic ligands can self-assemble into reduced-symmetry Pd2 L4 metallo-cages with anisotropic cavities, with implications for high specificity and affinity guest-binding. Mixtures of cage isomers can form, however, resulting in undesirable system heterogeneity. It is paramount to be able to design components that preferentially form a single isomer. Previous data suggested that computational methods could predict with reasonable accuracy whether unsymmetrical ligands would preferentially self-assemble into single cage isomers under constraints of geometrical mismatch. We successfully apply a collaborative computational and experimental workflow to mitigate costly trial-and-error synthetic approaches. Our rapid computational workflow constructs unsymmetrical ligands and their Pd2 L4 cage isomers, ranking the likelihood for exclusively forming cis-Pd2 L4 assemblies. From this narrowed search space, we successfully synthesised four new, low-symmetry, cis-Pd2 L4 cages.

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.

Gas Storage in Porous Molecular Materials

Molecules with permanent porosity in the solid state have been studied for decades. Porosity in these systems is governed by intrinsic pore space, as in cages or macrocycles, and extrinsic void space, created through loose, intermolecular solid-state packing. The development of permanently porous molecular materials, especially cages with organic or metal-organic composition, has seen increased interest over the past decade, and as such, incredibly high surface areas have been reported for these solids. Despite this, examples of these materials being explored for gas storage applications are relatively limited. This minireview outlines existing molecular systems that have been investigated for gas storage and highlights strategies that have been used to understand adsorption mechanisms in porous molecular materials.

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.

Directional asymmetry over multiple length scales in reticular porous materials

In nature and synthetic materials, asymmetry is a useful tool to create complex and functional systems constructed from a limited number of building blocks. Reticular chemistry has allowed the synthesis of a wide range of discrete and extended structures, from which modularity permits the controlled assembly of their constituents to generate asymmetric configurations of pores or architectures. In this perspective, we present the different strategies to impart directional asymmetry over nano/meso/macroscopic length scales in porous materials and the resulting novel properties and applications.

Design strategies for the controlled assembly of discrete and extended reticular materials with asymmetric configurations of pores or architectures.

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.

Computational discovery of molecular C60 encapsulants with an evolutionary algorithm

Computation is playing an increasing role in the discovery of materials, including supramolecular materials such as encapsulants. In this work, a function-led computational discovery using an evolutionary algorithm is used to find potential fullerene (C₆₀) encapsulants within the chemical space of porous organic cages. We find that the promising host cages for C₆₀ evolve over the simulations towards systems that share features such as the correct cavity size to host C₆₀, planar tri-topic aldehyde building blocks with a small number of rotational bonds, di-topic amine linkers with functionality on adjacent carbon atoms, high structural symmetry, and strong complex binding affinity towards C₆₀. The proposed cages are chemically feasible and similar to cages already present in the literature, helping to increase the likelihood of the future synthetic realisation of these predictions. The presented approach is generalisable and can be tailored to target a wide range of properties in molecular material systems.

Organic Imine Cages: Molecular Marriage and Applications

Imine condensation has been known to chemists for more than a century and is used extensively to synthesize large organic cages of defined shapes and sizes. Surprisingly, in the context of the synthetic methods for organic imine cages (OICs), a self-sorting/self-selection (molecular marriage) process has been overlooked over the years. Such processes are omnipresent in nature, from the creation of galaxies to the formation of the smallest building blocks of life (the cell). Such processes have the incredible ability to guide a system toward the formation of a specific product or products out of a collection of equally probable multiple possibilities. This Minireview sheds light on new opportunities in cage design offered by the self-sorting/self-selection protocol in OICs. Recent efforts to explore organic cages for various exciting new applications are discussed; for example, for detection of harmful small organic molecules, as templates for nucleation of metal nanoparticles (MNPs), and as proton-conducting materials.

Solvothermal synthesis of porous organic cage CC3 in the presence of dimethylformamide as solvent

Morphology, and crystal product of porous organic cage CC3, was modified by the use of a novel and non-traditional high dielectric constant solvent dimethyl formamide.