Diastereoselective Self-Assembly of Low-Symmetry Pdn L2n Nanocages through Coordination-Sphere Engineering

Metal-organic cages (MOCs) are popular host architectures assembled from ligands and metal ions/nodes. Assembling structurally complex, low-symmetry MOCs with anisotropic cavities can be limited by the formation of statistical isomer libraries. We set out to investigate the use of primary coordination-sphere engineering (CSE) to bias isomer selectivity within homo- and heteroleptic Pdn L2n cages. Unexpected differences in selectivities between alternative donor groups led us to recognise the significant impact of the second coordination sphere on isomer stabilities. From this, molecular-level insight into the origins of selectivity between cis and trans diastereoisomers was gained, highlighting the importance of both host-guest and host-solvent interactions, in addition to ligand design. This detailed understanding allows precision engineering of low-symmetry MOC assemblies without wholesale redesign of the ligand framework, and fundamentally provides a theoretical scaffold for the development of stimuli-responsive, shape-shifting MOCs.

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.

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.

Solution-processable polytriazoles from spirocyclic monomers for membrane-based hydrocarbon separations

The thermal distillation of crude oil mixtures is an energy-intensive process, accounting for nearly 1% of global energy consumption. Membrane-based separations are an appealing alternative or tandem process to distillation due to intrinsic energy efficiency advantages. We developed a family of spirocyclic polytriazoles from structurally diverse monomers for membrane applications. The resulting polymers were prepared by a convenient step-growth method using copper-catalysed azide-alkyne cycloaddition, providing very fast reaction rates, high molecular weights and solubilities in common organic solvents and non-interconnected microporosity. Fractionation of whole Arabian light crude oil and atmospheric tower bottom feeds using these materials enriched the low-boiling-point components and removed trace heteroatom and metal impurities (comparable performance with the lighter feed as the commercial polyimide, Matrimid), demonstrating opportunities to reduce the energy cost of crude oil distillation with tandem membrane processes. Membrane-based molecular separation under these demanding conditions is made possible by high thermal stability and a moderate level of dynamic chain mobility, leading to transient interconnections between micropores, as revealed by the calculations of static and swollen pore structures.

The effect of disorder in multi-component covalent organic frameworks

We examined the effect of two different types of linker distribution-random or correlated distribution-on the pore size and shape within single-layers of three multi-component COFs. We reveal a relationship between linker distribution and the porosity of COF solid solutions. The methods presented in this paper are generalisable and could be used in further studies to examine the properties of disordered framework materials.

Thin Film Composite Membranes with Regulated Crossover and Water Migration for Long-Life Aqueous Redox Flow Batteries

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.

Competitive aminal formation during the synthesis of a highly soluble, isopropyl-decorated imine porous organic cage

The synthesis of a new porous organic cage decorated with isopropyl moieties (CC21) was achieved from the reaction of triformylbenzene and an isopropyl functionalised diamine. Unlike structurally analogous porous organic cages, its synthesis proved challenging due to competitive aminal formation, rationalised using control experiments and computational modelling. The use of an additional amine was found to increase conversion to the desired cage.

Ion-Selective Microporous Polymer Membranes with Hydrogen-Bond and Salt-Bridge Networks for Aqueous Organic Redox Flow Batteries

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 that concurrently deliver low ionic resistance and high selectivity toward redox-active species are highly desired. Here, high-performance RFB membranes are fabricated from blends of carboxylate- and amidoxime-functionalized polymers of intrinsic microporosity, which 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.

Tetramine Aspect Ratio and Flexibility Determine Framework Symmetry for Zn8 L6 Self-Assembled Structures

We derive design principles for the assembly of rectangular tetramines into Zn8 L6 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 Zn8 L6 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.

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.

Controlling anisotropic properties by manipulating the orientation of chiral small molecules

Chiral π-conjugated molecules bring new functionality to technological applications and represent an exciting, rapidly expanding area of research. Their functional properties, such as the absorption and emission of circularly polarized light or the transport of spin-polarized electrons, are highly anisotropic. As a result, the orientation of chiral molecules critically determines the functionality and efficiency of chiral devices. Here we present a strategy to control the orientation of a small chiral molecule (2,2'-dicyano[6]helicene) by the use of organic and inorganic templating layers. Such templating layers can either force 2,2'-dicyano[6]helicene to adopt a face-on orientation and self-assemble into upright supramolecular columns oriented with their helical axis perpendicular to the substrate, or an edge-on orientation with parallel-lying supramolecular columns. Through such control, we show that low- and high-energy chiroptical responses can be independently 'turned on' or 'turned off'. The templating methodologies described here provide a simple way to engineer orientational control and, by association, anisotropic functional properties of chiral molecular systems for a range of emerging technologies.

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.

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.

Modeling the Effect of Defects and Disorder in Amorphous Metal-Organic Frameworks

Amorphous metal-organic frameworks (aMOFs) are a class of disordered framework materials with a defined local order given by the connectivity between inorganic nodes and organic linkers, but absent long-range order. The rational development of function for aMOFs is hindered by our limited understanding of the underlying structure-property relationships in these systems, a consequence of the absence of long-range order, which makes experimental characterization particularly challenging. Here, we use a versatile modeling approach to generate in silico structural models for an aMOF based on Fe trimers and 1,3,5-benzenetricarboxylate (BTC) linkers, Fe-BTC. We build a phase space for this material that includes nine amorphous phases with different degrees of defects and local order. These models are analyzed through a combination of structural analysis, pore analysis, and pair distribution functions. Therefore, we are able to systematically explore the effects of the variation of each of these features, both in isolation and combined, for a disordered MOF system, something that would not be possible through experiment alone. We find that the degree of local order has a greater impact on structure and properties than the degree of defects. The approach presented here is versatile and allows for the study of different structural features and MOF chemistries, enabling the derivation of design rules for the rational development of aMOFs.

Orientational self-sorting in cuboctahedral Pd cages

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.

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.

Long-Life Aqueous Organic Redox Flow Batteries Enabled by Amidoxime-Functionalized Ion-Selective Polymer Membranes

Redox flow batteries (RFBs) based on aqueous organic electrolytes are a promising technology for safe and cost‐effective large‐scale electrical energy storage. Membrane separators are a key component in RFBs, allowing fast conduction of charge‐carrier ions but minimizing the cross‐over of redox‐active species. Here, we report the molecular engineering of amidoxime‐functionalized Polymers of Intrinsic Microporosity (AO‐PIMs) by tuning their polymer chain topology and pore architecture to optimize membrane ion transport functions. AO‐PIM membranes are integrated with three emerging aqueous organic flow battery chemistries, and the synergetic integration of ion‐selective membranes with molecular engineered organic molecules in neutral‐pH electrolytes leads to significantly enhanced cycling stability.

Solution-Processable Redox-Active Polymers of Intrinsic Microporosity for Electrochemical Energy Storage

Redox-active organic materials have emerged as promising alternatives to conventional inorganic electrode materials in electrochemical devices for energy storage. However, the deployment of redox-active organic materials in practical lithium-ion battery devices is hindered by their undesired solubility in electrolyte solvents, sluggish charge transfer and mass transport, as well as processing complexity. Here, we report a new molecular engineering approach to prepare redox-active polymers of intrinsic microporosity (PIMs) that possess an open network of subnanometer pores and abundant accessible carbonyl-based redox sites for fast lithium-ion transport and storage. Redox-active PIMs can be solution-processed into thin films and polymer–carbon composites with a homogeneously dispersed microstructure while remaining insoluble in electrolyte solvents. Solution-processed redox-active PIM electrodes demonstrate improved cycling performance in lithium-ion batteries with no apparent capacity decay. Redox-active PIMs with combined properties of intrinsic microporosity, reversible redox activity, and solution processability may have broad utility in a variety of electrochemical devices for energy storage, sensors, and electronic applications.

Porous liquids - the future is looking emptier

The development of microporosity in the liquid state is leading to an inherent change in the way we approach applications of functional porosity, potentially allowing access to new processes by exploiting the fluidity of these new materials. By engineering permanent porosity into a liquid, over the transient intermolecular porosity in all liquids, it is possible to design and form a porous liquid. Since the concept was proposed in 2007, and the first examples realised in 2015, the field has seen rapid advances among the types and numbers of porous liquids developed, our understanding of the structure and properties, as well as improvements in gas uptake and molecular separations. However, despite these recent advances, the field is still young, and with only a few applications reported to date, the potential that porous liquids have to transform the field of microporous materials remains largely untapped. In this review, we will explore the theory and conception of porous liquids and cover major advances in the area, key experimental characterisation techniques and computational approaches that have been employed to understand these systems, and summarise the investigated applications of porous liquids that have been presented to date. We also outline an emerging discovery workflow with recommendations for the characterisation required at each stage to both confirm permanent porosity and fully understand the physical properties of the porous liquid.

Multivariate analysis of disorder in metal-organic frameworks

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.

Structural disorder in materials is challenging to characterise. Here, the authors use multivariate analysis of atomic pair distribution functions to study structural collapse and melting of metal–organic frameworks, revealing powerful mechanistic and kinetic insight.

Unlocking the computational design of metal-organic cages

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.

Emerging properties from mechanical tethering within a post-synthetically functionalised catenane scaffold

Using a post-synthetic modification strategy we have prepared a series of functionalised [2]catenanes to study the impact of mechanically-enforced proximity on functional group properties, including emission, electrochemistry and photoreactivity.

Explainable graph neural networks for organic cages

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

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

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

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

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.

A Family of Superhelicenes: Easily Tunable, Chiral Nanographenes by Merging Helicity with Planar π Systems

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.

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

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

Sterics and Hydrogen Bonding Control Stereochemistry and Self-Sorting in BINOL-Based Assemblies

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 Fe^II to Zn^II, or the incorporation of a longer linear ligand, favored heteroleptic structure formation.

HCGA: Highly comparative graph analysis for network phenotyping

Networks are widely used as mathematical models of complex systems across many scientific disciplines. Decades of work have produced a vast corpus of research characterizing the topological, combinatorial, statistical, and spectral properties of graphs. Each graph property can be thought of as a feature that captures important (and sometimes overlapping) characteristics of a network. In this paper, we introduce HCGA, a framework for highly comparative analysis of graph datasets that computes several thousands of graph features from any given network. HCGA also offers a suite of statistical learning and data analysis tools for automated identification and selection of important and interpretable features underpinning the characterization of graph datasets. We show that HCGA outperforms other methodologies on supervised classification tasks on benchmark datasets while retaining the interpretability of network features. We exemplify HCGA by predicting the charge transfer in organic semiconductors and clustering a dataset of neuronal morphology images.

Mixed hierarchical local structure in a disordered metal-organic framework

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.

Integrating Computational and Experimental Workflows for Accelerated Organic Materials Discovery

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

Fullerene Desymmetrization as a Means to Achieve Single-Enantiomer Electron Acceptors with Maximized Chiroptical Responsiveness

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 (g_ph  = 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.

Can we predict materials that can be synthesised?

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

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

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

High-Throughput Approaches for the Discovery of Supramolecular Organic Cages

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

N-Aryl-linked spirocyclic polymers for membrane separations of complex hydrocarbon mixtures

The fractionation of crude-oil mixtures through distillation is a large-scale, energy-intensive process. Membrane materials can avoid phase changes in such mixtures and thereby reduce the energy intensity of these thermal separations. With this application in mind, we created spirocyclic polymers with N-aryl bonds that demonstrated noninterconnected microporosity in the absence of ladder linkages. The resulting glassy polymer membranes demonstrated nonthermal membrane fractionation of light crude oil through a combination of class- and size-based "sorting" of molecules. We observed an enrichment of molecules lighter than 170 daltons corresponding to a carbon number of 12 or a boiling point less than 200°C in the permeate. Such scalable, selective membranes offer potential for the hybridization of energy-efficient technology with conventional processes such as distillation.

Switching between Local and Global Aromaticity in a Conjugated Macrocycle for High-Performance Organic Sodium-Ion Battery Anodes

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.