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Redox flow batteries (RFBs) are promising for large-scale long-duration energy storage owing to their inherent safety, decoupled power and energy, high efficiency, and longevity. Membranes constitute an important component that affects mass transport processes in RFBs, including ion transport, redox-species crossover, and the net volumetric transfer of supporting electrolytes. Hydrophilic microporous polymers, such as polymers of intrinsic microporosity (PIM), are demonstrated as next-generation ion-selective membranes in RFBs. However, the crossover of redox species and water migration through membranes are remaining challenges for battery longevity. Here, a facile strategy is reported for regulating mass transport and enhancing battery cycling stability by employing thin film composite (TFC) membranes prepared from a PIM polymer with optimized selective-layer thickness. Integration of these PIM-based TFC membranes with a variety of redox chemistries allows for the screening of suitable RFB systems that display high compatibility between membrane and redox couples, affording long-life operation with minimal capacity fade. Thickness optimization of TFC membranes further improves cycling performance and significantly restricts water transfer in selected RFB systems.

We derive design principles for the assembly of rectangular tetramines into Zn8L6 pseudo-cubic coordination cages. Because of the rectangular, as opposed to square, geometry of the ligand panels, and the possibility of either Δ or Λ handedness of each metal center at the eight corners of the pseudo-cube, many different cage diastereomers are possible. Each of the six tetra-aniline subcomponents investigated in this work assembled with zinc(II) and 2-formylpyridine in acetonitrile into a single Zn8L6 pseudo-cube diastereomer, however. Each product corresponded to one of four diastereomeric configurations, with T, Th, S6 or D3 symmetry. The preferred diastereomer for a given tetra-aniline subcomponent was shown to be dependent on its aspect ratio and conformational flexibility. Analysis of computationally modeled individual faces or whole pseudo-cubes provided insight as to why the observed diastereomers were favored.

Redox flow batteries (RFBs) have great potential for long-duration grid-scale energy storage. Ion conducting membranes are a crucial component in RFBs, allowing charge-carrying ions to transport while preventing the cross-mixing of redox couples. Commercial Nafion membranes are widely used in RFBs, but their unsatisfactory ionic and molecular selectivity as well as high costs limit the performance and the widespread deployment of this technology. To extend the longevity and reduce the cost of RFB systems, inexpensive ion-selective membranes are highly desired that concurrently deliver low ionic resistance and high selectivity towards redox-active species. In this work, high-performance RFB membranes are fabricated from blends of carboxylate- and amidoxime-functionalized polymers of intrinsic microporosity (PIMs) that exploit the beneficial properties of both polymers. The enthalpy-driven formation of cohesive interchain interactions, including hydrogen bonds and salt bridges, facilitates the microscopic miscibility of the blends, while ionizable functional groups within the sub-nanometer pores allow optimization of membrane ion transport functions. The resulting microporous membranes demonstrate fast cation conduction with low crossover of redox-active molecular species, enabling improved power ratings and reduced capacity fade in aqueous RFBs using anthraquinone and ferrocyanide as redox couples. This article is protected by copyright. All rights reserved.

Summary Host-guest complexation is an important supramolecular route to materials. Clear design rules have been developed for complexation in solution. This has proved more challenging for solid-state host-guest co-crystals because they often exhibit polymorphism, leading many researchers to focus instead on bonded frameworks, such as metal-organic frameworks. Here, we report an anthracene-based organic cage (1) that forms isoskeletal host-guest co-crystals with five similarly sized solid organic guests. The co-crystals were designed using inexpensive computational methods to identify appropriate guests that have packing coefficients (PCs) ranging from 44% to 50%, coupled with consideration of the guest shape. By complexing highly emissive BODIPY guests into the host structure, we enhanced its two-photon excited photoluminescent properties by a factor of six. Our crystal design approach was also transferrable to hard-to-design ternary organic crystals that were accessed by inserting specific guests into different sized voids in the host.

We designed a straightforward synthetic route towards a full‐fledged family of π‐extended helicenes: superhelicenes. They have two hexa‐peri‐hexabenzocoronenes (HBCs) in common that are connected via a central five‐membered ring. By means of structurally altering this 5‐membered ring, we realized a versatile library of molecular building blocks. Not only the superhelicene structure, but also their features are tuned with ease. In‐depth physico‐chemical characterizations served as a proof of concept thereof. The superhelicene enantiomers were separated, their circular dichroism was measured in preliminary studies and concluded with an enantiomeric assignment. Our work was rounded‐off by crystal structure analyses. Mixed stacks of M‐ and P‐isomers led to twisted molecular wires. Using such stacks, charge‐carrier mobilities were calculated, giving reason to expect outstanding hole transporting properties., We present the synthesis and in‐depth physico‐chemical characterization of a family of π‐extended helicenes that consist of HBCs fused via different five‐membered rings. Despite being only a small part of the compounds, the nature of the five‐membered rings has a huge impact on the properties of the whole molecule. We show the enantiomeric separation and circular dichroism properties together with X‐ray analysis and charge transport calculations.

Cuboctahedral coordination cages of the general formula [Pd12L24]24+ (L = low-symmetry ligand) were analyzed theoretically and experimentally. With 350 696 potential isomers, the structural space of these assemblies is vast. Orientational self-sorting refers to the preferential formation of particular isomers within the pool of potential structures. Geometric and computational analyses predict the preferred formation of cages with a cis arrangement at the metal centers. This prediction was corroborated experimentally by synthesizing a [Pd12L24]24+ cage with a bridging 3-(4-(pyridin-4-yl)phenyl)pyridine ligand. A crystallographic analysis of this assembly showed exclusive cis coordination of the 3- and the 4-pyridyl donor groups at the Pd2+ ions.

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.

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.

Metal–organic cages are macrocyclic structures that can possess an intrinsic void that can hold molecules for encapsulation, adsorption, sensing, and catalysis applications. As metal–organic cages may be comprised from nearly any combination of organic and metal-containing components, cages can form with diverse shapes and sizes, allowing for tuning toward targeted properties. Therefore, their near-infinite design space is almost impossible to explore through experimentation alone and computational design can play a crucial role in exploring new systems. Although high-throughput computational design and screening workflows have long been known as powerful tools in drug and materials discovery, their application in exploring metal–organic cages is more recent. We show examples of structure prediction and host–guest/catalytic property evaluation of metal–organic cages. These examples are facilitated by advances in methods that handle metal-containing systems with improved accuracy and are the beginning of the development of automated cage design workflows. We finally outline a scope for how high-throughput computational methods can assist and drive experimental decisions as the field pushes toward functional and complex metal–organic cages. In particular, we highlight the importance of considering realistic, flexible systems.

The rational design of disordered frameworks is an appealing route to target functional materials. However, intentional realisation of such materials relies on our ability to readily characterise and quantify structural disorder. Here, we use multivariate analysis of pair distribution functions to fingerprint and quantify the disorder within a series of compositionally identical metal-organic frameworks, possessing different crystalline, disordered, and amorphous structures. We find this approach can provide powerful insight into the kinetics and mechanism of structural collapse that links these materials. Our methodology is also extended to a very different system, namely the melting of a zeolitic imidazolate framework, to demonstrate the potential generality of this approach across many areas of disordered structural chemistry.

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.

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 realisation. 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 realisation. We trained a machine learning model by first collecting data on 12,553 molecules categorised either as `easy-to-synthesise' or `difficult-to-synthesise' by expert chemists with years of experience in organic synthesis. We used an approach to address the class imbalance present in our dataset, producing a binary classifier able to categorise easy-to-synthesise molecules with few false positives. We then used our model during computational screening for porous organic molecules to bias towards precursors whose easier synthesis requirements would make them promising candidates for experimental realisation and material development. We found that even by limiting precursors to those that are easier-to-synthesise, we are still able to identify cages with favourable, and even some rare, properties.

Unsymmetrical ditopic ligands can self‐assemble into reduced‐symmetry Pd2L4 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 Pd2L4 cage isomers, ranking the likelihood for exclusively forming cis‐Pd2L4 assemblies. From this narrowed search space, we successfully synthesised four new, low‐symmetry, cis‐Pd2L4 cages., Towards the development of sophisticated metal‐organic hosts, low‐symmetry metallo‐supramolecular cages assembled from unsymmetrical ligands are of emerging interest. Designing systems to avoid forming isomeric mixtures, however, is challenging. We report the use of a rapid computational workflow to predict self‐assembly outcomes, informing experimental decisions and optimising synthetic realisation of low‐symmetry, cis‐Pd2L4 cages.

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 foa 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., Steric and geometric constraints were used to design unsymmetrical ditopic ligands that form single Pd2L4 cage isomers with high fidelity.

The use of unsymmetrical components in metallo-supramolecular chemistry allows for low-symmetry architectures with anisotropic cavities toward guest-binding with high specificity and affinity. Unsymmetrical ditopic ligands mixed with Pd(II) have the potential to self-assemble into reduced symmetry Pd2L4 metallo-architectures. Mixtures of isomers can form, however, resulting in potentially undesirable heterogeneity within a system. Therefore 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 a single isomer under constraints of geometrical mismatch. We successfully apply a collaborative computational and experimental workflow to mitigate costly trial-and-error synthetic approaches. Our low-cost computational workflow rapidly constructs new unsymmetrical ligands (and Pd2L4 cage isomers) and ranks their likelihood for forming cis-Pd2L4 assemblies. From this narrowed search space, we successfully synthesised four new low-symmetry, cis-Pd2L4 cages, with cavities of different shapes and sizes.

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.

This chapter introduces the field of computational modelling of microporous materials and provides a foundation for reading the remaining chapters that focus on specific areas of porous materials modelling. Firstly, the different classes of porous materials are introduced along with the key application areas for these materials. Secondly, an overview is given of the different types of computational approaches that are applied in different areas of porous materials modelling.

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.

Solubilized fullerene derivatives have revolutionised 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. Here we separate 10 pairs of enantiomers from the 19 structural isomers of bis[60]PCBM, using them to elucidate important chiroptical structure-property relationships and demonstrating their application to a single enantiomer circularly polarized (CP) light detecting device. We find that larger chiroptical responses occur through inherent chirality of the fullerene cage and particularly through transitions with low CT character. When used in a single enantiomer organic field-effect transistor device, we demonstrate the potential to discriminate CP light with a fast light response time and with a very high photocurrent dissymmetry factor (gph = ±1.35). Our study thus provides key strategies to design fullerenes with large chiroptical responses for use as single enantiomer components of organic electronic devices. We anticipate that our data will position chiral fullerenes as an exciting material class for the growing field of chiral electronic technologies.

Molecular materials are challenging to design as their packing arrangement and hence properties are subject to subtle variations in the interplay of soft intermolecular interactions that are difficult to predict. The rational design of new molecular materials with tailored properties is currently hampered by the lack of knowledge of how a candidate molecule will pack in space and how we can control the polymorphs we can experimentally obtain. Here, we develop a simplified approach to aid the material design process, by the development of a screening process that is used to test 1344 helicene molecules that have potential as organic electronic materials. Our approach bridges the gap between single molecule design, molecular assembly, and the resulting charge-carrier mobilities. We find that fluorination significantly improves electron transport in the molecular material by up to 200%; the reference [6]helicene packing showed a mobility of 0.30 cm2 V-1 s-1, fluorination increased the mobility to up to 0.96 and 0.97 (13-fluoro[6]H and 4,13-difluoro[6]H), assuming an outer reorganisation energy of 0.30 eV. Side groups containing triple bonds largely lead to improved transfer integrals. We validate our screening approach through the use of crystal structure prediction to confirm the presence of favourable packing motifs to maximize charge mobility.

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.

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

The PLUMED consortium unifies developers and contributors to PLUMED, an open-source library for enhanced-sampling, free-energy calculations and the analysis of molecular dynamics simulations. Here, we outline our efforts to promote transparency and reproducibility by disseminating protocols for enhanced-sampling molecular simulations.

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.

Artificial intelligence is beginning to significantly increase the rate at which new materials are discovered, by influencing almost all aspects of the materials design process, especially structure and property prediction. Embracing more efficient, data-driven approaches has the potential to significantly increase the number of organic materials that can be screened for useful applications. However, there are various challenges, including representing extended materials in a machine-readable format and obtaining sufficient amounts of training data to generate useful predictive models. This chapter discusses some of the key artificial intelligence techniques that have been applied to organic material prediction and discovery and covers examples of the application of artificial intelligence to the fields of porous organic materials, organic electronics, and organic systems with other desired physical properties.

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.