93 results found
Abet V, Szczypiński FT, Little MA, et al., 2020, Corrigendum: Inducing Social Self-Sorting in Organic Cages To Tune The Shape of The Internal Cavity (Angewandte Chemie International Edition, (2020), 59, (16755-16763), 10.1002/anie.202007571), Angewandte Chemie - International Edition, Vol: 59, ISSN: 1433-7851
© 2020 Wiley-VCH GmbH The authors would like to draw attention to an error in Figure 5 of this Research Article. The top line of Figure 5 does not show the correct cage ([L1+2B1]); rather, [L1+2B1N] has been shown twice. The corrected figure is shown below. This mistake does not affect the scientific conclusions of the paper. Figure (Figure presented.) Side-, window-, and top-views of the electrostatic potential inside the cavities of the socially sorted [L1+2B1X] cages, mapped onto the 0.0004 a.u. total electron density isosurface calculated at the M06-2X/6-311+G(d,p) level of theory.
Peach RL, Arnaudon A, Schmidt J, et al., 2020, hcga: Highly Comparative Graph Analysis for network phenotyping
<jats:title>A<jats:sc>bstract</jats:sc></jats:title><jats:p>Networks are widely used as mathematical models of complex systems across many scientific disciplines, not only in biology and medicine but also in the social sciences, physics, computing and engineering. Decades of work have produced a vast corpus of research characterising the topological, combinatorial, statistical and spectral properties of graphs. Each graph property can be thought of as a feature that captures important (and some times overlapping) characteristics of a network. In the analysis of real-world graphs, it is crucial to integrate systematically a large number of diverse graph features in order to characterise and classify networks, as well as to aid network-based scientific discovery. In this paper, we introduce <jats:sc>hcga</jats:sc>, a framework for highly comparative analysis of graph data sets that computes several thousands of graph features from any given network. <jats:sc>hcga</jats:sc> also offers a suite of statistical learning and data analysis tools for automated identification and selection of important and interpretable features underpinning the characterisation of graph data sets. We show that <jats:sc>hcga</jats:sc> outperforms other methodologies on supervised classification tasks on benchmark data sets whilst retaining the interpretability of network features. We also illustrate how <jats:sc>hcga</jats:sc> can be used for network-based discovery through two examples where data is naturally represented as graphs: the clustering of a data set of images of neuronal morphologies, and a regression problem to predict charge transfer in organic semiconductors based on their structure. <jats:sc>hcga</jats:sc> is an open platform that can be expanded to include further graph properties and statistical learning tools to allow researchers to leverage the wide breadth of graph-theoretical resear
Jelfs K, Greenaway R, 2020, Integrating computational and experimental workflows for accelerated organic material discovery, Advanced Materials, ISSN: 0935-9648
Abet V, Szczypiński FT, Little MA, et al., 2020, Inducing Social Self-Sorting in Organic Cages To Tune The Shape of The Internal Cavity, Angewandte Chemie, Vol: 132, Pages: 16898-16906, ISSN: 0044-8249
© 2020 The Authors. Published by Wiley-VCH Verlag GmbH & Co. KGaA 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.
Abet V, Szczypiński FT, Little MA, et al., 2020, Inducing social self-sorting in organic cages to tune the shape of the internal cavity, Angewandte Chemie International Edition, Vol: 59, Pages: 16755-16763, ISSN: 1433-7851
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.
Greenaway R, Jelfs K, 2020, High-throughput approaches for the discovery of supramolecular organic cages, ChemPlusChem, Vol: 85, Pages: 1813-1823, ISSN: 2192-6506
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.
Eder S, Yoo D-J, Nogala W, et al., 2020, Switching between local and global aromaticity in a conjugated macrocycle for high-performance organic sodium-ion battery anodes, Angewandte Chemie International Edition, Vol: 59, Pages: 12958-12964, ISSN: 1433-7851
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, [188.8.131.52]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.
Thompson KA, Mathias R, Kim D, et al., 2020, N-Aryl-linked spirocyclic polymers for membrane separations of complex hydrocarbon mixtures, Sciene, Vol: 369, Pages: 310-315, ISSN: 0036-8075
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.
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.
Yuan Q, Santana Bonilla A, Zwijnenburg MA, et al., 2020, Molecular generation targeting desired electronic properties via deep generative models, Nanoscale, Vol: 12, Pages: 6744-6758, ISSN: 2040-3364
As we seek to discover new functional materials, we need ways to explore the vast chemical space of precursor building blocks, not only generating large numbers of possible building blocks to investigate, but trying to find non-obvious options, that we might not suggest by chemical experience alone. Artificial intelligence techniques provide a possible avenue to generate large numbers of organic building blocks for functional materials, and can even do so from very small initial libraries of known building blocks. Specifically, we demonstrate the application of deep recurrent neural networks for the exploration of the chemical space of building blocks for a test case of donor–acceptor oligomers with specific electronic properties. The recurrent neural network learned how to produce novel donor–acceptor oligomers by trading off between selected atomic substitutions, such as halogenation or methylation, and molecular features such as the oligomer's size. The electronic and structural properties of the generated oligomers can be tuned by sampling from different subsets of the training database, which enabled us to enrich the library of donor–acceptors towards desired properties. We generated approximately 1700 new donor–acceptor oligomers with a recurrent neural network tuned to target oligomers with a HOMO–LUMO gap <2 eV and a dipole moment <2 Debye, which could have potential application in organic photovoltaics.
Tan R, Wang A, Malpass-Evans R, et al., 2020, Hydrophilic microporous membranes for selective ion separation and flow-battery energy storage, Nature Materials, Vol: 19, Pages: 195-202, ISSN: 1476-1122
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 Tröger’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.
Miklitz M, Turcani L, Greenaway RL, et al., 2020, Computational discovery of molecular C60 encapsulants with an evolutionary algorithm, Communications Chemistry, Vol: 3, ISSN: 2399-3669
Computation is playing an increasing role in the discovery of materials, including supramolecular materials such as encapsulants. In this work, a function-led computational discovery using an evolutionary algorithm is used to find potential fullerene (C60) encapsulants within the chemical space of porous organic cages. We find that the promising host cages for C60 evolve over the simulations towards systems that share features such as the correct cavity size to host C60, planar tri-topic aldehyde building blocks with a small number of rotational bonds, di-topic amine linkers with functionality on adjacent carbon atoms, high structural symmetry, and strong complex binding affinity towards C60. The proposed cages are chemically feasible and similar to cages already present in the literature, helping to increase the likelihood of the future synthetic realisation of these predictions. The presented approach is generalisable and can be tailored to target a wide range of properties in molecular material systems.
Bennett S, Tarzia A, Zwijnenburg MA, et al., 2020, Chapter 12: Artificial Intelligence Applied to the Prediction of Organic Materials, RSC Theoretical and Computational Chemistry Series, Pages: 280-310
© 2020 The Royal Society of Chemistry. 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.
Berardo E, Miklitz M, Greenaway R, et al., 2020, Computational screening for nested organic cage complexes, Molecular Systems Design and Engineering, Vol: 5, Pages: 186-196, ISSN: 2058-9689
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.
Tan R, Wang A, Malpass-Evans R, et al., 2019, Hydrophilic microporous membranes for selective ion separation and flow-battery energy storage (December, 10.1038/S41563-019-0536-8, 2019), NATURE MATERIALS, Vol: 19, Pages: 251-251, ISSN: 1476-1122
Lewis JEM, Tarzia A, White AJP, et al., 2019, Conformational control of Pd2L4 assemblies with unsymmetrical ligands, Chemical Science, Vol: 11, Pages: 677-683, ISSN: 2041-6520
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.
Jelfs K, Greenaway RL, Santolini V, et al., 2019, From concept to crystals via prediction: multi‐component organic cage pots by social self‐sorting, Angewandte Chemie International Edition, Vol: 131, Pages: 16421-16427, ISSN: 1433-7851
We describe the a priori computational prediction and realization of multi‐component cage pots, starting with molecular predictions based on candidate precursors through to crystal structure prediction and synthesis using robotic screening. The molecules were formed by the social self‐sorting of a tri‐topic aldehyde with both a tri‐topic amine and di‐topic amine, without using orthogonal reactivity or precursors of the same topicity. Crystal structure prediction suggested a rich polymorphic landscape, where there was an overall preference for chiral recognition to form heterochiral rather than homochiral packings, with heterochiral pairs being more likely to pack window‐to‐window to form two‐component capsules. These crystal packing preferences were then observed in experimental crystal structures.
Meier CB, Clowes R, Berardo E, et al., 2019, Structurally Diverse Covalent Triazine-Based Framework Materials for Photocatalytic Hydrogen Evolution from Water, Chemistry of Materials, ISSN: 0897-4756
Jackson E, Miklitz M, Song Q, et al., 2019, Computational evaluation of the diffusion mechanisms for C8 aromatics in porous organic cages, The Journal of Physical Chemistry C: Energy Conversion and Storage, Optical and Electronic Devices, Interfaces, Nanomaterials, and Hard Matter, Vol: 123, Pages: 21011-21021, ISSN: 1932-7447
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.
Bonomi M, Bussi G, Camilloni C, et al., 2019, Promoting transparency and reproducibility in enhanced molecular simulations, Nature Methods, Vol: 16, Pages: 670-673, ISSN: 1548-7091
Teng B, Little MA, Hasell T, et al., 2019, Synthesis of a Large, Shape-Flexible, Solvatomorphic Porous Organic Cage, CRYSTAL GROWTH & DESIGN, Vol: 19, Pages: 3647-3651, ISSN: 1528-7483
Wilbraham L, Sprick RS, Jelfs KE, et al., 2019, Mapping binary copolymer property space with neural networks, CHEMICAL SCIENCE, Vol: 10, Pages: 4973-4984, ISSN: 2041-6520
Salerno F, Rice B, Schmidt JA, et al., 2019, The influence of nitrogen position on charge carrier mobility in enantiopure azahelicene crystals, Physical Chemistry Chemical Physics, Vol: 21, Pages: 5059-5067, ISSN: 1463-9076
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 azahelicene 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 azahelicene 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.
Turcani L, Greenaway RL, Jelfs KE, 2019, Machine learning for organic cage property prediction, Chemistry of Materials, Vol: 31, Pages: 714-727, ISSN: 0897-4756
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.
Miklitz M, Jelfs K, 2018, pywindow: automated structural analysis of molecular pores, Journal of Chemical Information and Modeling, Vol: 58, Pages: 2387-2391, ISSN: 1549-9596
Structural analysis of molecular pores can yield important information on their behavior in solution and in the solid state. We developed pywindow, a python package that enables the automated analysis of structural features of porous molecular materials, such as molecular cages. Our analysis includes the cavity diameter, number of windows, window diameters, and average molecular diameter. Molecular dynamics trajectories of molecular pores can also be analyzed to explore the influence of flexibility. We present the methodology, validation, and application of pywindow for the analysis of molecular pores, metal–organic polyhedra, and some instances of framework materials. pywindow is freely available from github.com/JelfsMaterialsGroup/pywindow.
Berardo E, Greenaway R, Turcani L, et al., 2018, Computationally-Inspired Discovery of an Unsymmetrical Porous Organic Cage, Nanoscale, Vol: 10, Pages: 22381-22388, ISSN: 2040-3364
A completely unsymmetrical porous organic cage was synthesised 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 ditopic 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 characterisation 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.
Berardo E, Miklitz M, Turcani L, et al., 2018, An evolutionary algorithm for the discovery of porous organic cages, Chemical Science, Vol: 9, Pages: 8513-8527, ISSN: 2041-6520
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 (<20%) and that the higher topicity building block in particular should use double bonds for rigidity. We can use the EA to explore what size ranges for precursors are required for achieving a given pore size in a cage and show that 16 Å pores, which are absent in the literature, should be synthetically achievable. Our EA implementation is adaptable and easily extendable, not only to target specific properties of porous organic cages, such as optimal encapsulants or molecular separation materials, but also to any easily calculable property of other molecular materials.
Turcani L, Berardo E, Jelfs KE, 2018, stk : A Python toolkit for supramolecular assembly, Journal of Computational Chemistry, Vol: 39, Pages: 1931-1942, ISSN: 0192-8651
A tool for the automated assembly, molecular optimization and property calculationof supramolecular materials is presented. stk is a modular, extensible and open-sourcePython library that provides a simple Python API and integration with third partycomputational codes. stk currently supports the construction of linear polymers, smalllinear 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 explorethe potential energy landscape of the assembled supramolecule and then calculatethe supramolecule’s structural features and properties. stk provides support for highthroughputscreening of large batches of supramolecules at a time. The source code ofthe program can be found at https://github.com/supramolecular-toolkit/stk.
Greenaway R, Santolini V, Bennison MJ, et al., 2018, High-throughput discovery of organic cages and catenanes using computational screening fused with robotic synthesis, Nature Communications, Vol: 9, ISSN: 2041-1723
Supramolecular synthesis is a powerful strategy for assembling complex molecules, but to do this by targeted design is challenging. This is because multicomponent assembly reactions have the potential to form a wide variety of products. High-throughput screening can explore a broad synthetic space, but this is inefficient and inelegant when applied blindly. Here we fuse computation with robotic synthesis to create a hybrid discovery workflow for discovering new organic cage molecules, and by extension, other supramolecular systems. A total of 78 precursor combinations were investigated by computation and experiment, leading to 33 cages that were formed cleanly in one-pot syntheses. Comparison of calculations with experimental outcomes across this broad library shows that computation has the power to focus experiments, for example by identifying linkers that are less likely to be reliable for cage formation. Screening also led to the unplanned discovery of a new cage topology—doubly bridged, triply interlocked cage catenanes.
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