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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., A sea of expanding shapes: A combined experimental–computational approach enabled the synthesis of low‐symmetry imine cages from mixtures of tetraaldehyde building blocks. This “social self‐sorting” approach was applied to obtain a family of new cages containing heteroatoms, showing that pores of varying geometries and surface chemistries may be reliably accessed.

Aromatic organic compounds can be used as electrode materials in rechargeable batteries and are expected to advance the development of both anode and cathode materials for sodium‐ion batteries (SIBs). However, most aromatic organic compounds assessed as anode materials in SIBs to date exhibit significant degradation issues under fast‐charge/discharge conditions and unsatisfying long‐term cycling performance. Now, a molecular design concept is presented for improving the stability of organic compounds for battery electrodes. The molecular design of the investigated compound, [2.2.2.2]paracyclophane‐1,9,17,25‐tetraene (PCT), can stabilize the neutral state by local aromaticity and the doubly reduced state by global aromaticity, resulting in an anode material with extraordinarily stable cycling performance and outstanding performance under fast‐charge/discharge conditions, demonstrating an exciting new path for the development of electrode materials for SIBs and other types of batteries., Aromatic stabilization of both states: A molecular design concept for highly stable organic battery electrode materials is presented using the conjugated macrocycle [2.2.2.2]paracyclophane‐1,9,17,25‐tetraene (PCT). The compound can stabilize both the neutral and reduced state by aromaticity, enabling batteries with extraordinarily stable cycling performance and outstanding performance under fast‐charge/discharge conditions.

Molecular dumbbells with organic cage capping units were synthesised via a multi-component imine condensation between a tri-topic amine and di- and tetra-topic aldehydes. This is an example of self-sorting, which can be rationalised by computational modelling.

With increasing interest in the potential utility of metallo-supramolecular architectures for applications as diverse as catalysis and drug delivery, the ability to develop more complex assemblies is keenly sought after. Despite this, symmetrical ligands have been utilised almost exclusively to simplify the self-assembly process as without a significant driving force a mixture of isomeric products will be obtained. Although a small number of unsymmetrical ligands have been shown to serendipitously form well-defined metallo-supramolecular assemblies, a more systematic study could provide generally applicable information to assist in the design of lower symmetry architectures. Pd2L4 cages are a popular class of metallo-supramolecular assembly; research seeking to introduce added complexity into their structure to further their functionality has resulted in a handful of examples of heteroleptic structures, whilst the use of unsymmetrical ligands remains underexplored. Herein we show that it is possible to design unsymmetrical ligands in which either steric or geometric constraints, or both, can be incorporated into ligand frameworks to ensure exclusive formation of single isomers of three-dimensional Pd2L4 metallo-supramolecular assemblies with high fidelity. In this manner it is possible to access Pd2L4 cage architectures of reduced symmetry, a concept that could allow for the controlled spatial segregation of different functionalities within these systems. The introduction of steric directing groups was also seen to have a profound effect on the cage structures, suggesting that simple ligand modifications could be used to engineer structural properties.

Here we demonstrate how the hydrogen-bonding ability of a BINOL-based dialdehyde subcomponent dictated the stereochemical outcome of its subsequent self-assembly into one diastereomeric helicate form when bearing free hydroxy groups, and another in the case of its methylated congener. The presence of methyl groups also altered the self-sorting behavior when mixed with another, short linear dialdehyde subcomponent, switching the outcome of the system from narcissistic to integrative self-sorting. In all cases, the axial chirality of the BINOL building block also dictated helicate metal center handedness during stereospecific self-assembly. A new family of stereochemically pure heteroleptic helicates were thus prepared using the new knowledge gained. We also found that switching from FeII to ZnII, or the incorporation of a longer linear ligand, favored heteroleptic structure formation.

Amorphous metal–organic frameworks (MOFs) are an emerging class of materials. However, their structural characterisation represents a significant challenge. Fe-BTC, and the commercial equivalent Basolite® F300, are MOFs with incredibly diverse catalytic ability, yet their disordered structures remain poorly understood. Here, we use advanced electron microscopy to identify a nanocomposite structure of Fe-BTC where nanocrystalline domains are embedded within an amorphous matrix, whilst synchrotron total scattering measurements reveal the extent of local atomic order within Fe-BTC. We use a polymerisation-based algorithm to generate an atomistic structure for Fe-BTC, the first example of this methodology applied to the amorphous MOF field outside the well-studied zeolitic imidazolate framework family. This demonstrates the applicability of this computational approach towards the modelling of other amorphous MOF systems with potential generality towards all MOF chemistries and connectivities. We find that the structures of Fe-BTC and Basolite® F300 can be represented by models containing a mixture of short- and medium-range order with a greater proportion of medium-range order in Basolite® F300 than in Fe-BTC. We conclude by discussing how our approach may allow for high-throughput computational discovery of functional, amorphous MOFs., The structures of amorphous MOFs are challenging to characterise. Here the authors use electron microscopy and pair distribution function methods, coupled with a polymerisation-based algorithm to determine the atomic structure of Fe-BTC, demonstrating the power of this computational approach.

Polymer-based membranes can be used for energy efficient gas separations. Successful exploitation of new materials requires accurate knowledge of the transport properties of all gases of interest. An open source database of such data is of significant benefit to the research community. The Membrane Society of Australasia (https://membrane-australasia.org/) hosts a database for experimentally measured and reported polymer gas permeabilities. However, the database is incomplete, limiting its potential use as a research tool. Here, missing values in the database were filled using machine learning (ML). The ML model was validated against gas permeability measurements that were not recorded in the database. Through imputing the missing data, it is possible to re-analyse historical polymers and look for potential “missed” candidates with promising gas selectivity. In addition, for systems with limited experimental data, ML using sparse features was performed, and we suggest that once the permeability of CO2 and/or O2 for a polymer has been measured, most other gas permeabilities and selectivities, including those for CO2/CH4 and CO2/N2, can be quantitatively estimated. This early insight into the gas permeability of a new system can be used at an initial stage of experimental measurements to rapidly identify polymer membranes worth further investigation.

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., Materials discovery is a crucial yet experimentally slow and wasteful process. We discuss how discovery can be accelerated by focusing on making predictions that are synthetically realisable.

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.

The extremely large number of unique polymer compositions that can be achieved through copolymerisation makes it an attractive strategy for tuning their optoelectronic properties. However, this same attribute also makes it challenging to explore the resulting property space and understand the range of properties that can be realised. In an effort to enable the rapid exploration of this space in the case of binary copolymers, we train a neural network using a tiered data generation strategy to accurately predict the optical and electronic properties of 350 000 binary copolymers that are, in principle, synthesizable from their dihalogen monomers via Yamamoto, or Suzuki-Miyaura and Stille coupling after one-step functionalisation. By extracting general features of this property space that would otherwise be obscured in smaller datasets, we identify simple models that effectively relate the properties of these copolymers to the homopolymers of their constituent monomers, and challenge common ideas behind copolymer design. We find that binary copolymerisation does not appear to allow access to regions of the optoelectronic property space that are not already sampled by the homopolymers, although it conceptually allows for more fine-grained property control. Using the large volume of data available, we test the hypothesis that copolymerisation of 'donor' and 'acceptor' monomers can result in copolymers with a lower optical gap than their related homopolymers. Overall, despite the prevalence of this concept in the literature, we observe that this phenomenon is relatively rare, and propose conditions that greatly enhance the likelihood of its experimental realisation. Finally, through a 'topographical' analysis of the co-polymer property space, we show how this large volume of data can be used to identify dominant monomers in specific regions of property space that may be amenable to a variety of applications, such as organic photovoltaics, light emitting diodes, and thermoelectrics.

The chemical space for novel electronic donor-acceptor oligomers with targeted properties was explored using deep generative models and transfer learning. A General Recurrent Neural Network model was trained from the ChEMBL database to generate chemically valid SMILES strings. The parameters of the General Recurrent Neural Network were fine-tuned via transfer learning using the electronic donor-acceptor database from the Computational Material Repository to generate novel donor-acceptor oligomers. Six different transfer learning models were developed with different subsets of the donor-acceptor database as training sets. We concluded that electronic properties such as HOMO-LUMO gaps and dipole moments of the training sets can be learned using the SMILES representation with deep generative models, and that the chemical space of the training sets can be efficiently explored. This approach identified approximately 1700 new molecules that have promising electronic properties (HOMO-LUMO gap

Two dissymmetric racemic analogues of the chiral porous organic cage, CC3, were isolated and unambiguously characterised as a racemate pair of the R,R,R,S,S,S and S,S,S,R,R,R-diastereomers (CC3-RS and CC3-SR). CC3-RS/CC3-SR equals the highest porosity measured for CC3 but is an order of magnitude more soluble, making it an excellent candidate for incorporation into a membrane for separation applications.

The dispersion of particulate porous solids in size-excluded liquids has emerged as a method to create Type III porous liquids, mostly using insoluble microporous materials such as metal-organic frameworks (MOFs) and zeolites. Here, we present the first examples of Type III porous liquids based on porous organic cages (POCs). By exploiting the solution processability of the POCs, racemic and quasiracemic cage microparticles were formed by chiral recognition. Dispersion of these porous microparticles in a range of size-excluded liquids, including oils and ionic liquids, formed stable POC-based Type III porous liquids. The flexible pairing between the solid POC particles and a carrier liquid allows the formation of a range of compositions, pore sizes, and other physicochemical properties to suit different applications and operating conditions. For example, we show that it is possible to produce porous liquids with relatively low viscosities (7-14 mpa∙s) or high thermal stability (325 °C). A 12.5 wt. % Type III porous liquid comprising racemic POC microparticles and an ionic liquid, [BPy][NTf2], shows a CO2 working capacity (104.30 μmol/gL) that is significantly higher than the neat ionic liquid (37.27 μmol/gL) between 25 °C and 100 °C. This liquid is colloidally stable and can be recycled at least 10 times without loss of CO2 capacity.

Membranes with fast and selective ion transport are widely used for water purification and devices for energy conversion and storage including fuel cells, redox flow batteries and electrochemical reactors. However, it remains challenging to design cost-effective, easily processed ion-conductive membranes with well-defined pore architectures. Here, we report a new approach to designing membranes with narrow molecular-sized channels and hydrophilic functionality that enable fast transport of salt ions and high size-exclusion selectivity towards small organic molecules. These membranes, based on polymers of intrinsic microporosity containing Troger’s base or amidoxime groups, demonstrate that exquisite control over subnanometre pore structure, the introduction of hydrophilic functional groups and thickness control all play important roles in achieving fast ion transport combined with high molecular selectivity. These membranes enable aqueous organic flow batteries with high energy efficiency and high capacity retention, suggesting their utility for a variety of energy-related devices and water purification processes. Ion-selective membranes are widely used for water purification and electrochemical energy devices but designing their pore architectures is challenging. Membranes with narrow channels and hydrophilic functionality are shown to exhibit salt ions transport and selectivity towards small organic molecules.

Solubilized fullerene derivatives have revolutionized the development of organic photovoltaic devices, acting as excellent electron acceptors. The addition of solubilizing addends to the fullerene cage results in a large number of isomers, which are generally employed as isomeric mixtures. Moreover, a significant number of these isomers are chiral, which further adds to the isomeric complexity. The opportunities presented by single-isomer, and particularly single-enantiomer, fullerenes in organic electronic materials and devices are poorly understood however. Here, ten pairs of enantiomers are separated from the 19 structural isomers of bis[60]phenyl-C61-butyric acid methyl ester, using them to elucidate important chiroptical relationships and demonstrating their application to a circularly polarized light (CPL)-detecting device. Larger chiroptical responses are found, occurring through the inherent chirality of the fullerene. When used in a single-enantiomer organic field-effect transistor, the potential to discriminate CPL with a fast light response time and with a very high photocurrent dissymmetry factor (gph = 1.27 ± 0.06) is demonstrated. This study thus provides key strategies to design fullerenes with large chiroptical responses for use as chiral components of organic electronic devices. It is anticipated that this data will position chiral fullerenes as an exciting material class for the growing field of chiral electronic technologies.

The development of adsorption and membrane-based separation technologies toward more energy and cost-efficient processes is a significant engineering problem facing the world today. An example of a process in need of improvement is the separation of C8 aromatics to recover para-xylene, which is the precursor to the widely used monomer terephthalic acid. Molecular simulations were used to investigate whether the separation of C8 aromatics can be carried out by the porous organic cages CC3 and CC13, both of which have been previously used in the fabrication of amorphous thin-film membranes. Metadynamics simulations showed significant differences in the energetic barriers to the diffusion of different C8 aromatics through the porous cages, especially for CC3. These differences imply that meta-xylene and ortho-xylene will take significantly longer to enter or leave the cages. Therefore, it may be possible to use membranes composed of these materials to separate ortho- and meta-xylene from para-xylene by size exclusion. Differences in the C8 aromatics’ diffusion barriers were caused by their different diffusion mechanisms, while the lower selectivity of CC13 was largely down to its more significant pore breathing. These observations will aid the future design of adsorbents and membrane systems with improved separation performance.

Supramolecular self-assembly has allowed the synthesis of beautiful and complex molecular architectures, such as cages, macrocycles, knots, catenanes, and rotaxanes. We focus here on porous organic cages, which are molecules that have an intrinsic cavity and multiple windows. These cages have been shown to be highly effective at molecular separations and encapsulations. We investigate the possibility of complexes where one cage sits within the cavity of another. We term this a `nested cage' complex. The design of such complexes is highly challenging, so we use computational screening to explore 8712 different pair combinations, running almost 0.5M calculations to sample the phase space of the cage conformations. Through analysing the binding energies of the assemblies, we identify highly energetically favourable pairs of cages in nested cage complexes. The vast majority of the most favourable complexes include the large imine cage reported by Gawronski and co-workers using a [8+12] reaction of 4-tert-butyl-2,6-diformylphenol and cis,cis-1,3,5-triaminocyclohexane. The most energetically favourable nested cage complex combines the Gawronski cage with a dodecaamide cage that has six vertices, which can sit in the six windows of the larger cage. We also identify cages that have favourable binding energies for self-catenation.

A structurally diverse family of 39 covalent triazine-based framework materials (CTFs) is synthesized by Suzuki-Miyaura polycondensation and tested as hydrogen evolution photocatalysts using a high-throughput workflow. The two best-performing CTFs are based on benzonitrile and dibenzo[b,d]thiophene sulfone linkers, respectively, with catalytic activities that are among the highest for this material class. The activities of the different CTFs are rationalized in terms of four variables: the predicted electron affinity, the predicted ionization potential, the optical gap, and the dispersibility of the CTFs particles in solution, as measured by optical transmittance. The electron affinity and dispersibility in solution are the best predictors of photocatalytic hydrogen evolution activity.

[Image: see text] Porous organic cages have emerged over the last 10 years as a subclass of functional microporous materials. However, among all of the organic cages reported, large multicomponent organic cages with 20 components or more are still rare. Here, we present an [8 + 12] porous organic imine cage, CC20, which has an apparent surface area up to 1752 m(2) g(–1), depending on the crystallization and activation conditions. The cage is solvatomorphic and displays distinct geometrical cage structures, caused by crystal-packing effects, in its crystal structures. This indicates that larger cages can display a certain range of shape flexibility in the solid state, while remaining shape persistent and porous.

Composed from discrete units, porous molecular materials (PMMs) possess unique properties not observed for conventional, extended, solids, such as solution processibility and permanent porosity in the liquid phase. However, identifying the origin of porosity is not a trivial process, especially for amorphous or liquid phases. Furthermore, the assembly of molecular components is typically governed by a subtle balance of weak intermolecular forces that makes structure prediction challenging. Accordingly, in this review we canvass the crucial role of molecular simulations in the characterisation and design of PMMs. We will outline strategies for modelling porosity in crystalline, amorphous and liquid phases and also describe the state-of-the-art methods used for high-throughput screening of large datasets to identify materials that exhibit novel performance characteristics.

We define a nomenclature for the classification of porous organic cage molecules, enumerating the 20 most probable topologies, 12 of which have been synthetically realised to date. We then discuss the computational challenges encountered when trying to predict the most likely topological outcomes from dynamic covalent chemistry (DCC) reactions of organic building blocks. This allows us to explore the extent to which comparing the internal energies of possible reaction outcomes is successful in predicting the topology for a series of 10 different building block combinations.

By synthesizing derivatives of a trans-1,2-diaminocyclohexane precursor, three new functionalized porous organic cages were prepared with different chemical functionalities on the cage periphery. The introduction of twelve methyl groups (CC16) resulted in frustration of the cage packing mode, which more than doubled the surface area compared to the parent cage, CC3. The analogous installation of twelve hydroxyl groups provided an imine cage (CC17) that combines permanent porosity with the potential for post-synthetic modification of the cage exterior. Finally, the incorporation of bulky dihydroethanoanthracene groups was found to direct self-assembly towards the formation of a larger [8+12] cage, rather than the expected [4+6], cage molecule (CC18). However, CC18 was found to be non-porous, most likely due to cage collapse upon desolvation.

We use machine learning to predict shape persistence and cavity size in porous organic cages. The majority of hypothetical organic cages suffer from a lack of shape persistence and as a result lack intrinsic porosity, rendering them unsuitable for many applications. We have created the largest computational database of these molecules to date, numbering 63,472 cages, formed through a range of reaction chemistries and in multiple topologies. We study our database and identify features which lead to the formation of shape persistent cages. We find that the imine condensation of trialdehydes and diamines in a [4+6] reaction is the most likely to result in shape persistent cages, whereas thiol reactions are most likely to give collapsed cages. Using this database, we develop machine learning models capable of predicting shape persistence with an accuracy of up to 93%, reducing the time taken to predict this property to milliseconds, and removing the need for specialist software. In addition, we develop machine learning models for two other key properties of these molecules, cavity size and symmetry. We provide open-source implementations of our models, together with the accompanying data sets, and an online tool giving users access to our models to easily obtain predictions for a hypothetical cage prior to a synthesis attempt.

The properties of an organic semiconductor are dependent on both the chemical structure of the molecule involved, and how it is arranged in the solid-state. It is challenging to extract the influence of each individual factor, as small changes in the molecular structure often dramatically change the crystal packing and hence solid-state structure. Here, we use calculations to explore the influence of the nitrogen position on the charge mobility of a chiral organic molecule when the crystal packing is kept constant. The transfer integrals for a series of enantiopure aza[6]helicene crystals sharing the same packing were analysed in order to identify the best supramolecular motifs to promote charge carrier mobility. The regioisomers considered differ only in the positioning of the nitrogen atom in the aromatic scaffold. The simulations showed that even this small change in the chemical structure has a strong effect on the charge transport in the crystal, leading to differences in charge mobility of up to one order of magnitude. Some aza[6]helicene isomers that were packed interlocked with each other showed high HOMO-HOMO integrals (up to 70 meV), whilst molecules arranged with translational symmetry generally afforded the highest LUMO-LUMO integrals (40 - 70 meV). As many of the results are not intuitively obvious, a computational approach provides additional insight into the design of new semiconducting organic materials.

A completely unsymmetrical porous organic cage was synthesized from a C2v symmetrical building block that was identified by a computational screen. The cage was formed through a 12-fold imine condensation of a tritopic C2v symmetric trialdehyde with a di-topic C2 symmetric diamine in a [4+6] reaction. The cage was rigid and microporous, as predicted by the simulations, with an apparent Brunauer-Emmett-Teller surface area of 578 m2 g-1. The reduced symmetry of the tritopic building block relative to its topicity meant there were 36 possible structural isomers of the cage. Experimental characterization suggests a single isomer with 12 unique imine environments, but techniques such as NMR could not conclusively identify the isomer. Computational structural and electronic analysis of the possible isomers was used to identify the most likely candidates, and hence to construct a 3-dimensional model of the amorphous solid. The rational design of unsymmetrical cages using building blocks with reduced symmetry offers new possibilities in controlling the degree of crystallinity, porosity, and solubility, of self-assembled materials.

An evolutionary algorithm is developed and used to search for shape persistent porous organic cages., The chemical and structural space of possible molecular materials is enormous, as they can, in principle, be built from any combination of organic building blocks. Here we have developed an evolutionary algorithm (EA) that can assist in the efficient exploration of chemical space for molecular materials, helping to guide synthesis to materials with promising applications. We demonstrate the utility of our EA to porous organic cages, predicting both promising targets and identifying the chemical features that emerge as important for a cage to be shape persistent or to adopt a particular cavity size. We identify that shape persistent cages require a low percentage of rotatable bonds in their precursors (

The hydrogen evolution activity of a polymeric photocatalyst was maximised by co-polymerisation, using both experimental and computational screening, for a family of 1,4-phenylene/2,5-thiophene co-polymers. The photocatalytic activity is the product of multiple material properties that are affected in different ways by the polymer composition and microstructure. For the first time, the photocatalytic activity was shown to be a function of the arrangement of the building blocks in the polymer chain as well as the overall composition. The maximum in hydrogen evolution for the co-polymer series appears to result from a trade-off between the fraction of light absorbed and the thermodynamic driving force for proton reduction and sacrificial electron donor oxidation, with the co-polymer of p-terphenyl and 2,5-thiophene showing the highest activity.

A tool for the automated assembly, molecular optimization and property calculation of supramolecular materials is presented. stk is a modular, extensible and open‐source Python library that provides a simple Python API and integration with third party computational codes. stk currently supports the construction of linear polymers, small linear oligomers, organic cages in multiple topologies and covalent organic frameworks (COFs) in multiple framework topologies, but is designed to be easy to extend to new, unrelated, supramolecules or new topologies. Extension to metal–organic frameworks (MOFs), metallocycles or supramolecules, such as catenanes, would be straightforward. Through integration with third party codes, stk offers the user the opportunity to explore the potential energy landscape of the assembled supramolecule and then calculate the supramolecule's structural features and properties. stk provides support for high‐throughput screening of large batches of supramolecules at a time. The source code of the program can be found at https://github.com/supramolecular-toolkit/stk. © 2018 The Authors. Journal of Computational Chemistry published by Wiley Periodicals, Inc.

We show that a [3 + 6] trigonal prismatic imine (a) cage can rearrange stoichiometrically and structurally to form a [6 + 12] cage (b) with a truncated tetrahedral shape. Molecular simulations rationalize why this rearrangement was only observed for the prismatic [3 + 6] cage TCC1 but not for the analogous [3 + 6] cages, TCC2 and TCC3. Solvent was found to be a dominant factor in driving this rearrangement.

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We performed a computational screening of previously reported porous molecular materials, including porous organic cages, cucurbiturils, cyclodextrins, and cryptophanes, for Xe/Kr separation. Our approach for rapid screening through analysis of single host molecules, rather than the solid state structure of the materials, is evaluated. We use a set of tools including in-house software for structural evaluations, electronic structure calculations for guest binding energies, and molecular dynamics and metadynamics simulations to study the effect of the hosts’ flexibility upon guest diffusion. Our final results confirm that the CC3 cage molecule, previously reported as high performing for Xe/Kr separation, is the most promising of this class of materials reported to date. The Noria molecule was also found to be promising, and we therefore synthesized two related Noria molecules and tested their performance for Xe/Kr separation in the laboratory.

Synthetic control over pore size and pore connectivity is the crowning achievement for porous metal-organic frameworks. The same level of control has not been achieved for molecular crystals, which are not defined by strong, directional intermolecular coordination bonds. Hence, molecular crystallization is inherently less predictable than framework crystallization, and there are fewer examples of ‘reticular synthesis’, where multiple building blocks can be assembled according to a common assembly motif. Here, we apply a chiral recognition strategy to a new family of tubular covalent cages, TCC1–TCC3, to create both 1-D porous nanotubes and 3-D, diamondoid pillared porous networks in a targeted way. The diamondoid networks are analogous to metal-organic frameworks prepared from tetrahedral metal nodes and linear, difunctional organic linkers. The crystal structures can be rationalized by computational lattice energy searches, which provide an in silico screening method to evaluate candidate molecular building blocks. These results are a blueprint for applying the ‘node and strut’ principles of reticular synthesis to molecular crystals.

© 2016 The Royal Society of Chemistry.The practical adsorption properties of molecular porous solids can be dominated by dynamic flexibility but these effects are still poorly understood. Here, we combine molecular simulations and experiments to rationalize the adsorption behavior of a flexible porous organic cage.

The porosity of a glass formed by melt-quenching a metal-organic framework, has been characterized by positron annihilation lifetime spectroscopy. The results reveal porosity intermediate between the related open and dense crystalline frameworks ZIF-4 and ZIF-zni. A structural model for the glass was constructed using an amorphous polymerization algorithm, providing additional insight into the gas-inaccessible nature of porosity and the possible applications of hybrid glasses.

A computational approach for the prediction of the open, metastable, conformations of porous organic molecules in the presence of solvent is developed.

Molecular dynamics simulations were used to investigate the diffusion of six small gas molecules in a crystalline porous organic cage, CC3. A flexible host model was used to simulate transient channel formation, the effects of which are reflected in the calculated diffusion coefficients for the six gases of 5.64 × 10–8, 5.94 × 10–9, 2.60 × 10–9, 9.60 × 10–9, 2.40 × 10–9, and 1.83 × 10–10 m2 s–1, respectively, for H2, N2, CO2, CH4, Kr, and Xe. By contrast, a larger gas molecule, SF6, was predicted to be unable to diffuse in the pores of this material. We introduce a new method—a void space histogram—to analyze dynamic pore topologies and to graphically illustrate the structural factors determining guest diffusion.

The photophysical properties of insoluble porous pyrene networks, which are central to their function, differ strongly from those of analogous soluble linear and branched polymers and dendrimers. This can be rationalized by the presence of strained closed rings in the networks. A combined experimental and computational approach was used to obtain atomic scale insight into the structure of amorphous conjugated microporous polymers. The optical absorption and fluorescence spectra of a series of pyrene-based materials were compared with theoretical time-dependent density functional theory predictions for model clusters. Comparison of computation and experiment sheds light on the probable structural chromophores in the various materials.

We synthesize a series of imine cage molecules where increasing the chain length of the alkanediamine precursor results in an odd–even alternation between [2 + 3] and [4 + 6] cage macrocycles. A computational procedure is developed to predict the thermodynamically preferred product and the lowest energy conformer, hence rationalizing the observed alternation and the 3D cage structures, based on knowledge of the precursors alone.

Separating organics without distillation Hydrocarbon distillation is a widespread and energy-intensive process. Membranes might offer an alternative approach, but few can survive immersion in organic solvents nor are they able to extract relatively small molecules. Thompson et al. developed a series of polymers of intrinsic microporosity that they used for membrane-based separations of organic compounds in an organic solvent (see the Perspective by Brennecke and Freeman). The new membrane has a molecular weight cutoff of 253 daltons, far lower than existing ones closer to 600 daltons. The polymers were used to separate light shale crude oil and succeeded in fractionation of molecular weights of about 170 daltons. Science this issue p. 310 ; see also p. 254

Organic dyes based on conjugated chromophores such as diketopyrrolopyrrole (DPP) have a large range of uses beyond providing colour to other materials, such as in dye-sensitized solar cells, dye-sensitized photoelectrochemical cells, dye-sensitized colloidal photocatalysts and organic photovoltaics. We perform a high-throughput virtual screening using the xTB family of density functional tight-binding methods to map the optoelectronic property space of ∼45 000 DPP dyes. The large volume of data at our disposal allows us to probe the difference between symmetric and asymmetric dyes and to identify the apparent boundaries of the optoelectronic property space for these dyes, as well as which substituents give access to particular combinations of properties. Finally, we use our dataset to screen for DPP dyes that can drive the reduction of protons to molecular hydrogen when illuminated as part of dye-sensitized photoelectrochemical cells or dye-sensitized colloidal photocatalysts, or as dyes for TiO2-based dye-sensitized solar cells.