High-throughput discovery of organic cages and catenanes using computational screening fused with robotic synthesis

Guest-induced narcissistic self-sorting in water via imine formation

Two anionic tetrahedral cages were self-assembled as the only observable products in weakly basic water via imine condensation. The success of the high-yielding formation of the cages in water relies on (i) multivalency enhancing the stability of the imine bond and affording these cages water compatibility and (ii) a guest template with a complementary size and geometry that provides a hydrophobic driving force by occupying the corresponding cage cavity. When all four precursors, namely two trisaldehydes and two trisamines, were combined in water, narcissistic self-sorting occurred when both guest templates were present. In organic media where the hydrophobic effect is absent, narcissistic self-sorting did not occur in the analogous cage systems, confirming the importance of guest templates.

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.

Multifold Post-Modification of Macrocycles and Cages by Isocyanate-Induced Azadefluorination Cyclisation

We present the multiple post-modification of organic macrocycles and cages, introducing functional groups into two- and three-dimensional supramolecular scaffolds bearing fluorine substituents, which opens up new possibilities in multi-step supramolecular chemistry employing the vast chemical space of readily available isocyanates. The mechanism and scope of the reaction that proceeds after isocyanate addition to the benzylamine motif via an azadefluorination cyclisation (ADFC) were investigated using DFT calculations, and a series of aromatic isocyanates with different electronic properties were tested. The compounds show excellent chemical stability and were fully characterised. They can be used for subsequent cross-coupling reactions, and ADFC can be used directly to generate cross-linked membranes from macrocycles or cages when using ditopic isocyanates. Single-crystal X-ray (SC-XRD) analysis shows the proof of the formation of the desired supramolecular entity together with the connectivity predicted by calculations and from 19F NMR shifts, allowing the late-stage functionalisation of self-assembled macrocycles and cages by ADFC.

Photoswitchable imines: aryliminopyrazoles quantitatively convert to long-lived Z-isomers with visible light

Arylimines offer promise in dynamic-covalent materials due to their recyclability and ease of synthesis. However, their light-triggered E/Z isomerism has received little attention. This is attributed to challenges that include low thermal stability of their metastable state (<60 s at 20 °C), incomplete photoswitching (<50% to the metastable state), and the need for UV light (≤365 nm). We overcome these limitations with a novel class of imine photoswitch, the aryliminopyrazoles (AIPs). These AIPs can be switched using visible light (470 nm), attain photostationary states with over 95% of the Z-isomer, exhibit great resistance to fatigue, and have thermal half-lives up to 19.2 hours at room temperature. Additionally, they display T-type and negative photochromism under visible light irradiation-a useful property. The photochromic properties, quantitative assembly and accessibility of precursors set these photoswitches apart from their azo-based analogues. These findings open avenues for next-generation photoresponsive dynamic-covalent materials driven solely by these new photochromic linkages and further exploration of photocontrolled dynamic combinatorial chemistry.

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.

Expanding Olefin-Linked Covalent Organic Frameworks toward Selective Photocatalytic Oxidation of Organic Sulfides

Olefin-linked covalent organic frameworks (COFs) have exhibited great potential in visible-light photocatalysis. In principle, expanding fully conjugated COFs can facilitate light absorption and charge transfer, leading to improved photocatalysis. Herein, three olefin-linked COFs with the same topology are synthesized by combining 2,4,6-trimethyl-1,3,5-triazine (TMT) with 1,3,5-triformylbenzene (TFB), 1,3,5-tris(4-formylphenyl)benzene (TFPB), and 1,3,5-tris(4-formylphenylethynyl)benzene (TFPEB), namely, TMT-TFB-COF, TMT-TFPB-COF, and TMT-TFPEB-COF, respectively. From TMT-TFB-COF to TMT-TFPB-COF, expanding phenyl rings provides only limited expansion for π-conjugation due to the steric effect of structural twisting. However, from TMT-TFPB-COF to TMT-TFPEB-COF, the insertion of acetylenes eliminates the steric effect and provides more delocalized π-electrons. As such, TMT-TFPEB-COF exhibits the best optoelectronic properties among these three olefin-linked COFs. Consequently, the photocatalytic performance of TMT-TFPEB-COF is much better than those of TMT-TFB-COF and TMT-TFPB-COF on the oxidation of organic sulfides into sulfoxides with oxygen. The desirable reusability and substrate compatibility of the TMT-TFPEB-COF photocatalyst are further confirmed. The selective formation of organic sulfoxides over TMT-TFPEB-COF under blue light irradiation proceeds via both electron- and energy-transfer pathways. This work highlights a rational design of expanding the π-conjugation of fully conjugated COFs toward selective visible-light photocatalysis.

Modular, multi-robot integration of laboratories: an autonomous workflow for solid-state chemistry

Automation can transform productivity in research activities that use liquid handling, such as organic synthesis, but it has made less impact in materials laboratories, which require sample preparation steps and a range of solid-state characterization techniques. For example, powder X-ray diffraction (PXRD) is a key method in materials and pharmaceutical chemistry, but its end-to-end automation is challenging because it involves solid powder handling and sample processing. Here we present a fully autonomous solid-state workflow for PXRD experiments that can match or even surpass manual data quality, encompassing crystal growth, sample preparation, and automated data capture. The workflow involves 12 steps performed by a team of three multipurpose robots, illustrating the power of flexible, modular automation to integrate complex, multitask laboratories.

Dynamic covalent synthesis

Dynamic covalent synthesis aims to precisely control the assembly of simple building blocks linked by reversible covalent bonds to generate a single, structurally complex, product. In recent years, considerable progress in the programmability of dynamic covalent systems has enabled easy access to a broad range of assemblies, including macrocycles, shape-persistent cages, unconventional foldamers and mechanically-interlocked species (catenanes, knots, etc.). The reversibility of the covalent linkages can be either switched off to yield stable, isolable products or activated by specific physico-chemical stimuli, allowing the assemblies to adapt and respond to environmental changes in a controlled manner. This activatable dynamic property makes dynamic covalent assemblies particularly attractive for the design of complex matter, smart chemical systems, out-of-equilibrium systems, and molecular devices.

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.

Frontiers of molecular crystal structure prediction for pharmaceuticals and functional organic materials

The reliability of organic molecular crystal structure prediction has improved tremendously in recent years. Crystal structure predictions for small, mostly rigid molecules are quickly becoming routine. Structure predictions for larger, highly flexible molecules are more challenging, but their crystal structures can also now be predicted with increasing rates of success. These advances are ushering in a new era where crystal structure prediction drives the experimental discovery of new solid forms. After briefly discussing the computational methods that enable successful crystal structure prediction, this perspective presents case studies from the literature that demonstrate how state-of-the-art crystal structure prediction can transform how scientists approach problems involving the organic solid state. Applications to pharmaceuticals, porous organic materials, photomechanical crystals, organic semi-conductors, and nuclear magnetic resonance crystallography are included. Finally, efforts to improve our understanding of which predicted crystal structures can actually be produced experimentally and other outstanding challenges are discussed.

Controlling the Crystallisation and Hydration State of Crystalline Porous Organic Salts

Crystalline porous organic salts (CPOS) are a subclass of molecular crystals. The low solubility of CPOS and their building blocks limits the choice of crystallisation solvents to water or polar alcohols, hindering the isolation, scale-up, and scope of the porous material. In this work, high throughput screening was used to expand the solvent scope, resulting in the identification of a new porous salt, CPOS-7, formed from tetrakis(4-sulfophenyl)methane (TSPM) and tetrakis(4-aminophenyl)methane (TAPM). CPOS-7 does not form with standard solvents for CPOS, rather a hydrated phase (Hydrate2920) previously reported is isolated. Initial attempts to translate the crystallisation to batch led to challenges with loss of crystallinity and Hydrate2920 forming favorably in the presence of excess water. Using acetic acid as a dehydrating agent hindered formation of Hydrate2920 and furthermore allowed for direct conversion to CPOS-7. To allow for direct formation of CPOS-7 in high crystallinity flow chemistry was used for the first time to circumvent the issues found in batch. CPOS-7 and Hydrate2920 were shown to have promise for water and CO2 capture, with CPOS-7 having a CO2 uptake of 4.3 mmol/g at 195 K, making it one of the most porous CPOS reported to date.

Recent applications of organic cages in sensing and separation processes in solution

Organic cages are three-dimensional polycyclic compounds of great interest in the scientific community due to their unique features, which generally include simple synthesis based on the dynamic covalent chemistry strategies, structural tunability and high selectivity. In this feature article, we present the advances over the last ten years in the application of organic cages as chemosensors or components in chemosensing devices for the determination of analytes (pollutants, analytes of biological interest) in complex aqueous media including wine, fruit juice, urine. Details on the recent applications of organic cages as selective (back-)extractants or masking agents for potential applications in relevant separation processes, such as the plutonium and uranium recovery by extraction, are also provided. Over the last ten years, organic cages with permanent porosity in the liquid and solid states have been highly appreciated as porous materials able to discriminate molecules of different sizes. These features, combined with good solvent processability and film-forming tendency, have proved useful in the fabrication of membranes for gas separation, solvent nanofiltration and water remediation processes. An overview of the recent applications of organic cages in membrane separation technologies is given.

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.

Reversible CO2 Fixation and Release by a Trinuclear Zn(II) Cryptate Complex and Operando Analysis of the Complex Structure

Metal complexes inspired by carbonic anhydrase (CA), which is a metalloenzyme containing Zn(II), have been investigated as alternatives for CO2 fixation systems operating under ambient temperature and pressure conditions. In this study, we designed a trinuclear Zn(II) cryptate complex (Zn3 L) and demonstrated rapid CO2 fixation with carbonation of CO2 using Zn3 L. The CO2 fixation performance of Zn3 L surpassed that of a standard CO2 absorbent, KOH(aq) solution, under conditions of the same solute concentration. In addition, the reaction achieved operation without support addition of base, which has been often required in systems of CA-inspired complexes. Fixed CO2 was released by protonating polyazacryptate ligand (L) and breaking the complex structure, and deprotonation of L induced the reconstruction of Zn3 L, allowing it to refix CO2 . This reaction mechanism was proposed based on the analysis of operando extended X-ray absorption fine structure spectroscopy. Zn3 L also demonstrated the ability to capture dilute CO2 from air, and the volume of CO2 captured by Zn3 L was approximately 2.6 times that captured by the KOH(aq) solution. Our Zn3 L exhibited three valuable properties: rapid CO2 fixation without a base, reversibility, and ability to capture dilute CO2 ; thus Zn3 L is a promising candidate as CO2 fixatives.

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.

Industrial Separation Challenges: How Does Supramolecular Chemistry Help?

The chemical industry and the chemical processes underscoring it are under intense scrutiny as the demands for the transition to more sustainable and environmentally friendly practices are increasing. Traditional industrial separation systems, such as thermally driven distillation for hydrocarbon purification, are energy intensive. The development of more energy efficient separation technologies is thus emerging as a critical need, as is the creation of new materials that may permit a transition away from classic distillation-based separations. In this Perspective, we focus on porous organic cages and macrocycles that can adsorb guest molecules selectively through various host-guest interactions and permit molecular sieving behavior at the molecular level. Specifically, we summarize the recent advances where receptor-based adsorbent materials have been shown to be effective for industrially relevant hydrocarbon separations, highlighting the underlying host-guest interactions that impart selectivity and permit the observed separations. This approach to sustainable separations is currently in its infancy. Nevertheless, several receptor-based adsorbent materials with extrinsic/intrinsic voids or special functional groups have been reported in recent years that can selectively capture various targeted guest molecules. We believe that the understanding of the interactions that drive selectivity at a molecular level accruing from these initial systems will permit an ever-more-effective "bottom-up" design of tailored molecular sieves that, in due course, will allow adsorbent material-based approaches to separations to transition from the laboratory into an industrial setting.

Efficient Multigram Procedure for the Synthesis of Large Hydrazone-linked Molecular Cages

Covalently linked molecular cages can provide significant advantages (including, but not limited to enhanced thermal and chemical stability) over metal-linked coordination cages. Yet, while large coordination cages can now be created routinely, it is still challenging to create chemically robust, covalently linked molecular cages with large internal cavities. This fundamental challenge has made it difficult, for example, to introduce endohedral functional groups into covalent cages to enhance their practical utility (e.g., for selective guest recognition or catalysis), since the cavities would have simply been filled up with such endohedral functional groups in most cases. Here we now report the synthesis of some of the largest known covalently linked molecular tetrahedra. Our new covalent cages all contain 12 peripheral functional groups, which keep them soluble. They are formed from a common vertex, which aligns the hydrazide functions required for the hydrazone linkages with atropisomerism. While we previously reported this vertex as a building block for the smallest member of our hydrazone-linked tetrahedra, our original synthesis was not feasible to be carried out on the larger scales required to successfully access the larger tetrahedra. To overcome this synthetic challenge, we now present a greatly improved synthesis of our vertex, which only requires a single chromatographic step (compared to 3 chromatographic purification steps, which were needed for the initial synthesis). Our new synthetic route enabled us to create a whole family of molecular cages with increasing size (all linked with hydrolytically stable hydrazone bonds), with our largest covalent cage featuring p-quarterphenyl linkers and the ability to encapsulate a hypothetical sphere of approximately 3 nm in diameter. These results now open up the possibility to introduce functional groups required for selective recognition and catalysis into chemically robust covalent cages (without blocking the cavities of the covalent cages).

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.

Regulating π-Conjugation in sp2 -Carbon-Linked Covalent Organic Frameworks for Efficient Metal-Free CO2 Photoreduction with H2 O

The development of sp² -carbon-linked covalent organic frameworks (sp² c-COFs) as artificial photocatalysts for solar-driven conversion of CO₂ into chemical feedstock has captured growing attention, but catalytic performance has been significantly limited by their intrinsic organic linkages. Here, a simple, yet efficient approach is reported to improve the CO₂ photoreduction on metal-free sp² c-COFs by rationally regulating their intrinsic π-conjugation. The incorporation of ethynyl groups into conjugated skeletons affords a significant improvement in π-conjugation and facilitates the photogenerated charge separation and transfer, thereby boosting the CO₂ photoreduction in a solid-gas mode with only water vapor and CO₂ . The resultant CO production rate reaches as high as 382.0 µmol g^-1  h^-1 , ranking at the top among all additive-free CO₂ photoreduction catalysts. The simple modulation approach not only enables to achieve enhanced CO₂ reduction performance but also simultaneously gives a rise to extend the understanding of structure-property relationship and offer new possibilities for the development of new π-conjugated COF-based artificial photocatalysts.

Dimeric and trimeric catenation of giant chiral [8 + 12] imine cubes driven by weak supramolecular interactions

Mechanically interlocked structures, such as catenanes and rotaxanes, are fascinating synthetic targets and some are used for molecular switches and machines. Today, the vast majority of catenated structures are built upon macrocycles and only a very few examples of three-dimensional shape-persistent organic cages forming such structures have been reported. However, the catenation in all these cases was based on a thermodynamically favoured π-π-stacking under certain reaction conditions. Here, we show that catenane formation can be induced by adding methoxy or thiomethyl groups to one of the precursors during the synthesis of chiral [8 + 12] imine cubes, giving dimeric and trimeric catenated organic cages. To elucidate the underlying driving forces, we reacted 11 differently 1,4-disubstituted terephthaldehydes with a chiral triamino tribenzotriquinacene under various conditions to study whether monomeric cages or catenated cage dimers are the preferred products. We find that catenation is mainly directed by weak interactions derived from the substituents rather than by π-stacking.

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.

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.

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.

Into the Unknown: How Computation Can Help Explore Uncharted Material Space

Novel functional materials are urgently needed to help combat the major global challenges facing humanity, such as climate change and resource scarcity. Yet, the traditional experimental materials discovery process is slow and the material space at our disposal is too vast to effectively explore using intuition-guided experimentation alone. Most experimental materials discovery programs necessarily focus on exploring the local space of known materials, so we are not fully exploiting the enormous potential material space, where more novel materials with unique properties may exist. Computation, facilitated by improvements in open-source software and databases, as well as computer hardware has the potential to significantly accelerate the rational development of materials, but all too often is only used to postrationalize experimental observations. Thus, the true predictive power of computation, where theory leads experimentation, is not fully utilized. Here, we discuss the challenges to successful implementation of computation-driven materials discovery workflows, and then focus on the progress of the field, with a particular emphasis on the challenges to reaching novel materials.

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.

Graph-based Automated Macro-Molecule Assembly

We present a general molecular framework assembly algorithm that takes a largely arbitrary molecular fragment database and a user-supplied target template graph as input. Automatic assembly of molecular fragments from the database, following a prescribed, user-supplied set of connection rules, then turns the template graph into an actual, chemically reasonable molecular framework. Assembly capabilities of our algorithm are tested by producing several abstract, closed-loop shapes. To indicate a few of many possible application areas we demonstrate a host-guest complex and a road toward catalysis. Postassembly substituent exchange can be used to produce electric fields of desired values at desired points inside the framework or at its surface as a stepping stone toward rationally designed, artificial heterogeneous catalysts.

Insights into Chemically Fueled Supramolecular Polymers

Supramolecular polymerization can be controlled in space and time by chemical fuels. A nonassembled monomer is activated by the fuel and subsequently self-assembles into a polymer. Deactivation of the molecule either in solution or inside the polymer leads to disassembly. Whereas biology has already mastered this approach, fully artificial examples have only appeared in the past decade. Here, we map the available literature examples into four distinct regimes depending on their activation/deactivation rates and the equivalents of deactivating fuel. We present increasingly complex mathematical models, first considering only the chemical activation/deactivation rates (i.e., transient activation) and later including the full details of the isodesmic or cooperative supramolecular processes (i.e., transient self-assembly). We finish by showing that sustained oscillations are possible in chemically fueled cooperative supramolecular polymerization and provide mechanistic insights. We hope our models encourage the quantification of activation, deactivation, assembly, and disassembly kinetics in future studies.

Computational Modeling of Supramolecular Metallo-organic Cages-Challenges and Opportunities

Self-assembled metallo-organic cages have emerged as promising biomimetic platforms that can encapsulate whole substrates akin to an enzyme active site. Extensive experimental work has enabled access to a variety of structures, with a few notable examples showing catalytic behavior. However, computational investigations of metallo-organic cages are scarce, not least due to the challenges associated with their modeling and the lack of accurate and efficient protocols to evaluate these systems. In this review, we discuss key molecular principles governing the design of functional metallo-organic cages, from the assembly of building blocks through binding and catalysis. For each of these processes, computational protocols will be reviewed, considering their inherent strengths and weaknesses. We will demonstrate that while each approach may have its own specific pitfalls, they can be a powerful tool for rationalizing experimental observables and to guide synthetic efforts. To illustrate this point, we present several examples where modeling has helped to elucidate fundamental principles behind molecular recognition and reactivity. We highlight the importance of combining computational and experimental efforts to speed up supramolecular catalyst design while reducing time and resources.

Oligopyrrolic Cages: From Classic Molecular Constructs to Chemically Responsive Polytopic Receptors

Conspectus"Functional molecular systems", discrete and self-assembled constructs where control over molecular recognition, structure, bonding, transport, release, catalytic activity, etc., is readily achieved, are a topic of current interest. Within this broad paradigm, oligopyrrolic cages have garnered attention due to their responsive recognition features. Due to the presence of slightly polar pyrrole subunits which can also behave as hydrogen-bonding donors, these oligopyrrolic cages are potential receptors for various polarized species. In this Account, we summarize recent advances involving the syntheses and study of (1) covalent oligopyrrolic macrobicyclic cages, (2) oligopyrrolic metallacages, and (3) oligopyrrolic noncovalently linked cages. Considered in concert, these molecular constructs have allowed advances in applied supramolecular chemistry; to date, they have been exploited for selective guest encapsulation studies, anion binding and ion-channel formation, and gas absorption, among other applications. While key findings from others will be noted, in this Account will focus on our own contributions to the chemistry of discrete oligopyrrolic macrocycles and their use in supramolecular host-guest chemistry and sensing applications. In terms of specifics, we will detail how oligopyrrole cages with well-defined molecular geometries permit reversible guest binding under ambient conditions and how the incorporation of pyrrole subunits within larger superstructures allows effective control over anion/conjugate acid binding activity under ambient conditions. We will also provide examples that show how derivatization of these rudimentary macrocyclic cores with various sterically congested β-substituted oligopyrroles can provide entry into more complex supramolecular architectures. In addition, we will detail how hybrid systems that include heterocycles other than pyrrole, such as pyridine and naphthyridine, can be used to create self-assembled materials that show promise as gas-absorbing materials and colorimetric reversible sensors. Studies involving oligopyrrolic polymetallic cages and oligopyrrolic supramolecular cages will also be reviewed. First, we will discuss all-carbon-linked oligopyrrolic bicycles and continue on to present systems linked via amines and imines linkages. Finally, we will summarize recent work on pyrrolic cages created through the use of metal centers or various noncovalent interactions. We hope that this Account will provide researchers with an initial foundation for understanding oligopyrrolic cage chemistry, thereby allowing for further advances in the area. It is expected that the fundamental design and recognition principles made in the area of oligopyrrole cages, as exemplified by our contributions, will be of general use to researchers targeting the design of functional molecular systems. As such, we have structured this Account so as to summarize the past while setting the stage for the future.

Porous Liquids: Computational Design for Targeted Gas Adsorption

In this Perspective, we present the unique gas adsorption capabilities of porous liquids (PLs) and the value of complex computational methods in the design of PL compositions. Traditionally, liquids only contain transient pore space between molecules that limit long-term gas capture. However, PLs are stable fluids that that contain permanent porosity due to the combination of a rigid porous host structure and a solvent. PLs exhibit remarkable adsorption and separation properties, including increased solubility and selectivity. The unique gas adsorption properties of PLs are based on their structure, which exhibits multiple gas binding sites in the pore and on the cage surface, varying binding mechanisms including hydrogen-bonding and π-π interactions, and selective diffusion in the solvent. Tunable PL compositions will require fundamental investigations of competitive gas binding mechanisms, thermal effects on binding site stability, and the role of nanoconfinement on gas and solvent diffusion that can be accelerated through molecular modeling. With these new insights PLs promise to be an exceptional material class with tunable properties for targeted gas adsorption.

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.

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.

From Platform to Knowledge Graph: Evolution of Laboratory Automation

High-fidelity computer-aided experimentation is becoming more accessible with the development of computing power and artificial intelligence tools. The advancement of experimental hardware also empowers researchers to reach a level of accuracy that was not possible in the past. Marching toward the next generation of self-driving laboratories, the orchestration of both resources lies at the focal point of autonomous discovery in chemical science. To achieve such a goal, algorithmically accessible data representations and standardized communication protocols are indispensable. In this perspective, we recategorize the recently introduced approach based on Materials Acceleration Platforms into five functional components and discuss recent case studies that focus on the data representation and exchange scheme between different components. Emerging technologies for interoperable data representation and multi-agent systems are also discussed with their recent applications in chemical automation. We hypothesize that knowledge graph technology, orchestrating semantic web technologies and multi-agent systems, will be the driving force to bring data to knowledge, evolving our way of automating the laboratory.

Selective separation of planar and non-planar hydrocarbons using an aqueous Pd6 interlocked cage

Polycyclic aromatic hydrocarbons (PAHs) find multiple applications ranging from fabric dyes to optoelectronic materials. Hydrogenation of PAHs is often employed for their purification or derivatization. However, separation of PAHs from their hydrogenated analogues is challenging because of their similar physical properties. An example of such is the separation of 9,10-dihydroanthracene from phenanthrene/anthracene which requires fractional distillation at high temperature (∼340 °C) to obtain pure anthracene/phenanthrene in coal industry. Herein we demonstrate a new approach for this separation at room temperature using a water-soluble interlocked cage (1) as extracting agent by host–guest chemistry. The cage was obtained by self-assembly of a triimidazole donor L·HNO₃ with cis-[(tmeda)Pd(NO₃)₂] (M) [tmeda = N,N,N′,N′-tetramethylethane-1,2-diamine]. 1 has a triply interlocked structure with an inner cavity capable of selectively binding planar aromatic guests.

We report here a triply interlocked cage with the ability to encapsulate planar guests in aqueous medium. This property was then employed to efficiently separate planar and non-planar aromatic hydrocarbons by aqueous extraction.

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.

Failure-Experiment-Supported Optimization of Poorly Reproducible Synthetic Conditions for Novel Lanthanide Metal-Organic Frameworks with Two-Dimensional Secondary Building Units*

Novel metal-organic frameworks containing lanthanide double-layer-based secondary building units (KGF-3) were synthesized by using machine learning (ML). Isolating pure KGF-3 was challenging, and the synthesis was not reproducible because impurity phases were frequently obtained under the same synthetic conditions. Thus, dominant factors for the synthesis of KGF-3 were identified, and its synthetic conditions were optimized by using two ML techniques. Cluster analysis was used to classify the obtained powder X-ray diffractometry patterns of the products and thus automatically determine whether the experiments were successful. Decision-tree analysis was used to visualize the experimental results, after extracting factors that mainly affected the synthetic reproducibility. Water-adsorption isotherms revealed that KGF-3 possesses unique hydrophilic pores. Impedance measurements demonstrated good proton conductivities (σ=5.2×10^-4  S cm^-1 for KGF-3(Y)) at a high temperature (363 K) and relative humidity of 95 % RH.

Accelerate Synthesis of Metal-Organic Frameworks by a Robotic Platform and Bayesian Optimization

Synthesis of materials with desired structures, e.g., metal-organic frameworks (MOFs), involves optimization of highly complex chemical and reaction spaces due to multiple choices of chemical elements and reaction parameters/routes. Traditionally, realizing such an aim requires rapid screening of these nonlinear spaces by experimental conduction with human intuition, which is quite inefficient and may cause errors or bias. In this work, we report a platform that integrates a synthesis robot with the Bayesian optimization (BO) algorithm to accelerate the synthesis of MOFs. This robotic platform consists of a direct laser writing apparatus, precursor injecting and Joule-heating components. It can automate the MOFs synthesis upon fed reaction parameters that are recommended by the BO algorithm. Without any prior knowledge, this integrated platform continuously improves the crystallinity of ZIF-67, a demo MOF employed in this study, as the number of operation iterations increases. This work represents a methodology enabled by a data-driven synthesis robot, which achieves the goal of material synthesis with targeted structures, thus greatly shortening the reaction time and reducing energy consumption. It can be easily generalized to other material systems, thus paving a new route to the autonomous discovery of a variety of materials in a cost-effective way in the future.

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.

Optical Fingerprint Classification of Single Upconversion Nanoparticles by Deep Learning

Highly controlled synthesis of upconversion nanoparticles (UCNPs) can be achieved in the heterogeneous design, so that a library of optical properties can be arbitrarily produced by depositing multiple lanthanide ions. Such a control offers the potential in creating nanoscale barcodes carrying high-capacity information. With the increasing creation of optical information, it poses more challenges in decoding them in an accurate, high-throughput, and speedy fashion. Here, we reported that the deep-learning approach can recognize the complexity of the optical fingerprints from different UCNPs. Under a wide-field microscope, the lifetime profiles of hundreds of single nanoparticles can be collected at once, which offers a sufficient amount of data to develop deep-learning algorithms. We demonstrated that high accuracies of over 90% can be achieved in classifying 14 kinds of UCNPs. This work suggests new opportunities in handling the diverse properties of nanoscale optical barcodes toward the establishment of vast luminescent information carriers.

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.

The changing state of porous materials

Porous materials contain regions of empty space into which guest molecules can be selectively adsorbed and sometimes chemically transformed. This has made them useful in both industrial and domestic applications, ranging from gas separation, energy storage and ion exchange to heterogeneous catalysis and green chemistry. Porous materials are often ordered (crystalline) solids. Order-or uniformity-is frequently held to be advantageous, or even pivotal, to our ability to engineer useful properties in a rational way. Here we highlight the growing evidence that topological disorder can be useful in creating alternative properties in porous materials. In particular, we highlight here several concepts for the creation of novel porous liquids, rationalize routes to porous glasses and provide perspectives on applications for porous liquids and glasses.

Expanding the Tool Kit of Automated Flow Synthesis: Development of In-line Flash Chromatography Purification

Recent advancements in in-line extraction and purification technology have enabled complex multistep synthesis in continuous flow reactor systems. However, for the large scope of chemical reactions that yield mixtures of products or residual starting materials, off-line purification is still required to isolate the desired compound. We present the in-line integration of a commercial automated flash chromatography system with a flow reactor for the continuous synthesis and isolation of product(s). A proof-of-principle study was performed to validate the system and test the durability of the column cartridges, performing an automated sequence of 100 runs over 2 days. Three diverse reaction systems that highlight the advantages of flow synthesis were successfully applied with in-line normal- or reversed-phase flash chromatography, continuously isolating products with 97-99% purity. Productivity of up to 9.9 mmol/h was achieved, isolating gram quantities of pure product from a feed of crude reaction mixture. Herein, we describe the development and optimization of the systems and suggest guidelines for selecting reactions well suited to in-line flash chromatography.

One-Pot and Shape-Controlled Synthesis of Organic Cages

Organic cages are fascinating because of their well-defined 3D cavities, excellent stability, and accessible post-modification. However, the synthesis is normally realized by fragment coupling approach in low yields. Herein, we report one-pot, gram-scale and shape-controlled synthesis of two covalent organic cages (box-shaped [4]cage and triangular prism-shaped [2]cage) in yields of 46 % and 52 %, involving direct condensation of triangular 1,3,5-tris(2,4-dimethoxyphenyl)benzene monomer with paraformaldehyde and isobutyraldehyde, respectively. The cages can convert into high-yielding per-hydroxylated analogues. The [2]cage can be utilized as gas chromatographic stationary phase for high-resolution separation of benzene/cyclohexane and toluene/methylcyclohexane. By changing the central moiety of the triangular monomer and/or aldehyde, this synthetic method would have the potential to be a general strategy to access diverse cages with tunable shape, size, and electronic properties.