Imperial College London

Dr Becky Greenaway

Faculty of Natural SciencesDepartment of Chemistry

Senior Lecturer
 
 
 
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Contact

 

r.greenaway Website

 
 
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Location

 

401CMolecular Sciences Research HubWhite City Campus

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Summary

 

Publications

Publication Type
Year
to

40 results found

Brand MC, Trowell HG, Fuchter MJ, Greenaway RLet al., 2024, Incorporating photoresponses into porous liquids, Chemistry: A European Journal, Vol: 30, Pages: 1-7, ISSN: 0947-6539

Porous liquids combine the properties of a porous solid with those of a liquid, creating a porous flowable media. Since their discovery, these materials have gathered widespread interest within the scientific community, with substantial numbers of new systems being discovered, often with a focus on increasing the pore volume and gas capacity. Which begs the question, what does the future hold for porous liquids? Recently, the first examples of photoresponsive porous liquids have emerged, allowing changes in porosity to be observed under UV irradiation. Here, we expand on our previous report of photoresponsive porous liquids and explore the conceptualisation of responsive porous liquids and how these materials could be developed with the ability to respond to light, thereby offering a potential mechanism of controllable uptake and release in these systems. This concept article summarises different approaches that could be used to incorporate a photoresponse in a porous liquid before discussing recently reported systems, alongside important factors to consider in their design. Finally, by taking inspiration from the methods used to translate porous solids into the liquid state, combined with the field of photoresponsive materials, we discuss potential strategies that could be employed to realise further examples of photoresponsive porous liquids.

Journal article

Basford AR, Bennett SK, Xiao M, Turcani L, Allen J, Jelfs KE, Greenaway RLet al., 2024, Streamlining the automated discovery of porous organic cages, Chemical Science, ISSN: 2041-6520

Self-assembly through dynamic covalent chemistry (DCC) can yield a range of multi-component organic assemblies. The reversibility and dynamic nature of DCC has made prediction of reaction outcome particularly difficult and thus slows the discovery rate of new organic materials. In addition, traditional experimental processes are time-consuming and often rely on serendipity. Here, we present a streamlined hybrid workflow that combines automated high-throughput experimentation, automated data analysis, and computational modelling, to accelerate the discovery process of one particular subclass of molecular organic materials, porous organic cages. We demonstrate how the design and implementation of this workflow aids in the identification of organic cages with desirable properties. The curation of a precursor library of 55 tri- and di-topic aldehyde and amine precursors enabled the experimental screening of 366 imine condensation reactions experimentally, and 1464 hypothetical organic cage outcomes to be computationally modelled. From the screen, 225 cages were identified experimentally using mass spectrometry, 54 of which were cleanly formed as a single topology as determined by both turbidity measurements and 1H NMR spectroscopy. Integration of these characterisation methods into a fully automated Python pipeline, named cagey, led to over a 350-fold decrease in the time required for data analysis. This work highlights the advantages of combining automated synthesis, characterisation, and analysis, for large-scale data curation towards an accessible data-driven materials discovery approach.

Journal article

Egleston B, Greenaway RL, 2023, Liquids with permanent macroporosity, Angewandte Chemie International Edition, Vol: 62, ISSN: 1433-7851

Permanent macropores (>50 nm) had not been reported in the liquid state until a recent report by Tao Li and coworkers, describing a synthetic strategy to form a porous liquid with dual micro-macroporosity. This is prepared by producing hierarchically porous particles that are surface coated and fluidised by dispersion. Surface micropores enable permanent porosity by steric exclusion of the fluid phase. The material has a considerable water uptake capacity (27% w/w) due to large (480 nm) unoccupied macropores. This also enables switching of thermal conductivity on uptake of water. These are new properties translated from porous solids to the liquid state.

Journal article

Greenaway RLL, Jelfs KEE, Spivey ACC, Yaliraki SNNet al., 2023, From alchemist to AI chemist, NATURE REVIEWS CHEMISTRY, Vol: 7, Pages: 527-528

Journal article

Kearsey RJ, Tarzia A, Little MA, Brand MC, Clowes R, Jelfs KE, Cooper AI, Greenaway RLet al., 2023, Competitive aminal formation during the synthesis of a highly soluble, isopropyl-decorated imine porous organic cage., Chemical Communications, Vol: 59, Pages: 3731-3734, ISSN: 1359-7345

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.

Journal article

Brand M, Rankin N, Cooper A, Greenaway Ret al., 2023, Photoresponsive type III porous liquids, Chemistry: A European Journal, Vol: 29, Pages: 1-5, ISSN: 0947-6539

Porous materials are the subject of extensive research because of potential applications in areas such as gas adsorption and molecular separations. Until recently, most porous materials were solids, but there is now an emerging class of materials known as porous liquids. The incorporation of intrinsic porosity or cavities in a liquid can result in free-flowing materials that are capable of gas uptakes that are significantly higher than conventional non-porous liquids. A handful of porous liquids have also been investigated for gas separations. Until now, the release of gas from porous liquids has relied on molecular displacement (e.g., by adding small solvent molecules), pressure or temperature swings, or sonication. Here, we explore a new method of gas release which involves photoisomerisable porous liquids comprising a photoresponsive MOF dispersed in an ionic liquid. This results in the selective uptake of CO2 over CH4 and allows gas release to be controlled by using UV light.

Journal article

Egleston BD, Mroz A, Jelfs KE, Greenaway RLet al., 2022, Porous liquids - the future is looking emptier, Chemical Science, Vol: 13, Pages: 5042-5054, ISSN: 2041-6520

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.

Journal article

Ollerton K, Greenaway RL, Slater AG, 2021, Enabling Technology for Supramolecular Chemistry, FRONTIERS IN CHEMISTRY, Vol: 9, ISSN: 2296-2646

Journal article

Bennett S, Szczypiński F, Turcani L, Briggs M, Greenaway RL, Jelfs Ket al., 2021, Materials precursor score: modelling chemists' intuition for the synthetic accessibility of porous organic cage precursors, Journal of Chemical Information and Modeling, Vol: 61, Pages: 4342-4356, ISSN: 1549-9596

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.

Journal article

Brand M, Greenwell F, Clowes R, Egleston B, Kai A, Cooper A, Bennett T, Greenaway Ret al., 2021, Melt-quenched porous organic cage glasses, Journal of Materials Chemistry A, Vol: 9, Pages: 19807-19816, ISSN: 2050-7488

The discrete molecular nature of porous organic cages (POCs) has allowed us to direct the formation ofcrystalline materials by crystal engineering. It has also been possible to create porous amorphous solidsby deliberately disrupting the crystalline packing, either with chemical modification or by processing.More recently, organic cages were used to form isotropic porous liquids. However, the connectionbetween solid and liquid states of POCs, and the glass state, are almost completely unexplored. Here, weinvestigate the melting and glass-forming behaviour of a range of organic cages, including both shapepersistentPOCs formed by imine condensation, and reduced and synthetically post-modified aminePOCs that are more flexible and lack shape-persistence. The organic cages exhibited melting andquenching of the resultant liquids provides molecular glasses. One of these molecular glasses exhibitedimproved gas uptake for both CO2 and CH4 compared to the starting amorphous cage. In addition,foaming of the liquid in one case resulted in a more stable and less soluble glass, which demonstratesthe potential for an alternative approach to forming materials such as membranes without solutionprocessing.

Journal article

Kai A, Egleston BD, Tarzia A, Clowes R, Briggs ME, Jelfs KE, Cooper AI, Greenaway RLet al., 2021, Modular Type III porous liquids based on porous organic cage microparticles, Advanced Functional Materials, Vol: 31, Pages: 1-11, ISSN: 1616-301X

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 and zeolites. Here, the first examples of Type III porous liquids based on porous organic cages (POCs) are presented. By exploiting the solution processability of the POCs, racemic and quasiracemic cage microparticles are formed by chiral recognition. Dispersion of these porous microparticles in a range of size-excluded liquids, including oils and ionic liquids, forms 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, it is shown that porous liquids with relatively low viscosities or high thermal stability can be produced. 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−1) that is significantly higher than the neat ionic liquid (37.27 µmol gL−1) between 25 and 100 °C. This liquid is colloidally stable and can be recycled at least ten times without loss of CO2 capacity.

Journal article

Bennett S, Szczypiński F, Turcani L, Briggs M, Greenaway RL, Jelfs Ket al., 2021, Materials Precursor Score: Modelling Chemists' Intuition for the Synthetic Accessibility of Porous Organic Cages

<jats:p>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.</jats:p>

Journal article

Greenaway R, Jelfs K, 2021, Integrating computational and experimental workflows for accelerated organic material discovery, Advanced Materials, Vol: 33, Pages: 1-19, ISSN: 0935-9648

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 towards 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 realised. In this progress report, 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.

Journal article

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.

Journal article

Egleston BD, Brand MC, Greenwell F, Briggs ME, James SL, Cooper A, Crawford DE, Greenaway RLet al., 2020, Continuous and scalable synthesis of a porous organic cage by twin screw extrusion (TSE), CHEMICAL SCIENCE, Vol: 11, Pages: 6582-6589, ISSN: 2041-6520

Journal article

Egleston BD, Luzyanin KV, Brand MC, Clowes R, Briggs ME, Greenaway RL, Cooper AIet al., 2020, Controlling gas selectivity in molecular porous liquids by tuning the cage window size, Angewandte Chemie International Edition, Vol: 59, Pages: 7362-7366, ISSN: 1433-7851

Control of pore window size is the standard approach for tuning gas selectivity in porous solids. Here, we present the first example where this is translated into a molecular porous liquid formed from organic cage molecules. Reduction of the cage window size by chemical synthesis switches the selectivity from Xe‐selective to CH4‐selective, which is understood using 129Xe, 1H, and pulsed‐field gradient NMR spectroscopy.

Journal article

Greenaway RL, Santolini V, Szczypinski FT, Bennison MJ, Little MA, Marsh A, Jelfs KE, Cooper Aet al., 2020, Organic cage dumbbells, Chemistry: A European Journal, Vol: 26, Pages: 3718-3722, ISSN: 0947-6539

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.

Journal article

Miklitz M, Turcani L, Greenaway RL, Jelfs Ket 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.

Journal article

Berardo E, Miklitz M, Greenaway R, Cooper A, Jelfs Ket 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.

Journal article

Miklitz M, Turcani L, Greenaway RL, Jelfs Ket al., 2019, Computational Discovery of Molecular C60 Encapsulants with an Evolutionary Algorithm

<jats:p>A function-led computational discovery using an evolutionary algorithm was used to find potential fullerene (C60) encapsulants within the chemical space of porous organic cages. This makes use of a tailored fitness function that includes consideration of the interaction energy between the cage and the C60 molecule, the shape persistence of the cage, and the symmetry of the assemblies. 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 highly generalisable and can be tailored to target a wide range of properties in molecular encapsulants or other molecular material systems.</jats:p>

Journal article

Jelfs K, Greenaway RL, Santolini V, Pulido A, Little MA, Alston BM, Briggs ME, Day GM, Cooper AIet 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.

Journal article

Miklitz M, Turcani L, Greenaway RL, Jelfs Ket al., 2019, Computational Discovery of Molecular C60 Encapsulants with an Evolutionary Algorithm

<jats:p>A function-led computational discovery using an evolutionary algorithm was used to find potential fullerene (C60) encapsulants within the chemical space of porous organic cages. This makes use of a tailored fitness function that includes consideration of the interaction energy between the cage and the C60 molecule, the shape persistence of the cage, and the symmetry of the assemblies. 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 highly generalisable and can be tailored to target a wide range of properties in molecular encapsulants or other molecular material systems.</jats:p>

Journal article

Kearsey RJ, Alston BM, Briggs ME, Greenaway RL, Cooper AIet al., 2019, Accelerated robotic discovery of type II porous liquids, Chemical Science, Vol: 10, Pages: 9454-9465, ISSN: 2041-6520

Porous liquids are an emerging class of materials and to date little is known about how to best design their properties. For example, bulky solvents are required that are size-excluded from the pores in the liquid, along with high concentrations of the porous component, but both of these factors may also contribute to higher viscosities, which are undesirable. Hence, the inherent multivariate nature of porous liquids makes them amenable to high-throughput optimisation strategies. Here we develop a high-throughput robotic workflow, encompassing the synthesis, characterisation and property testing of highly-soluble, vertex-disordered porous organic cages dissolved in a range of cavity-excluded solvents. As a result, we identified 29 cage–solvent combinations that combine both higher cage-cavity concentrations and more acceptable carrier solvents than the best previous examples. The most soluble materials gave three times the pore concentration of the best previously reported scrambled cage porous liquid, as demonstrated by increased gas uptake. We were also able to explore alternative methods for gas capture and release, including liberation of the gas by increasing the temperature. We also found that porous liquids can form gels at higher concentrations, trapping the gas in the pores, which could have potential applications in gas storage and transportation.

Journal article

Berardo E, Greenaway RL, Miklitz M, Cooper AI, Jelfs Ket al., 2019, Computational Screening for Nested Organic Cage Complexes

<jats:p>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.</jats:p>

Journal article

Greenaway RL, Santolini V, Pulido A, Little MA, Alston BM, Briggs M, Day G, Cooper AI, Jelfs Ket al., 2019, From Concept to Crystals via Prediction: Multi-Component Organic Cage Pots by Social Self-Sorting

<jats:p>We describe the <jats:italic>a priori </jats:italic>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. </jats:p><jats:p />

Journal article

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.

Journal article

Berardo E, Greenaway R, Turcani L, Alston B, Bennison M, Miklitz M, Clowes R, Briggs M, Cooper A, Jelfs Ket 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.

Journal article

Turcani L, Greenaway RL, Jelfs K, 2018, Machine Learning for Organic Cage Property Prediction

<jats:p>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.</jats:p>

Journal article

Turcani L, Greenaway RL, Jelfs K, 2018, Machine Learning for Organic Cage Property Prediction

<jats:p>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.</jats:p>

Journal article

Berardo E, Greenaway RL, Turcani L, Alston BM, Bennison MJ, Miklitz M, Clowes R, Briggs ME, Cooper AI, Jelfs Ket al., 2018, Computationally-Inspired Discovery of an Unsymmetrical Porous Organic Cage

<jats:p>A completely unsymmetrical porous organic cage was synthesized from a C<jats:italic>2v </jats:italic>symmetrical building block that was identified by a computational screen. The cage was formed through a 12-fold imine condensation of a tritopic C<jats:italic>2v </jats:italic>symmetric trialdehyde with a di-topic C<jats:italic>2 </jats:italic>symmetric diamine in a [4+6] reaction. The cage was rigid and microporous, as predicted by the simulations, with an apparent Brunauer-Emmett-Teller surface area of 578 m2 g-1. The reduced symmetry of the tritopic building block relative to its topicity meant there were 36 possible structural isomers of the cage. Experimental characterization suggests a single isomer with 12 unique imine environments, but techniques such as NMR could not conclusively identify the isomer. Computational structural and electronic analysis of the possible isomers was used to identify the most likely candidates, and hence to construct a 3-dimensional model of the amorphous solid. The rational design of unsymmetrical cages using building blocks with reduced symmetry offers new possibilities in controlling the degree of crystallinity, porosity, and solubility, of self-assembled materials.</jats:p>

Journal article

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