25 results found
Bennett S, Szczypiński FT, Turcani L, et al., 2021, Materials Precursor Score: Modeling Chemists' Intuition for the Synthetic Accessibility of Porous Organic Cage Precursors., J Chem Inf Model, Vol: 61, Pages: 4342-4356
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.
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.
Kai A, Egleston BD, Tarzia A, et al., 2021, Modular Type III Porous Liquids Based on Porous Organic Cage Microparticles, ADVANCED FUNCTIONAL MATERIALS, ISSN: 1616-301X
Bennett S, Szczypiński F, Turcani L, et al., 2021, Materials precursor score: modelling chemists' intuition for the synthetic accessibility of porous organic cage precursors, Journal of Chemical Information and Modeling, 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.
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.
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.
Egleston BD, Brand MC, Greenwell F, et 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
Egleston BD, Luzyanin KV, Brand MC, et 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.
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.
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.
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.
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.
Kearsey RJ, Alston BM, Briggs ME, et 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.
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.
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.
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, Pages: 1-11, 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.
Pugh CJ, Santolini V, Greenaway RL, et al., 2018, Cage doubling: solvent-mediated re-equilibration of a [3+6] prismatic organic cage to a large [6+12] truncated tetrahedron, Crystal Growth and Design, Vol: 18, Pages: 2759-2764, ISSN: 1528-7483
We show that a [3 + 6] trigonal prismatic imine (a) cage can rearrange stoichiometrically and structurally to form a [6 + 12] cage (b) with a truncated tetrahedral shape. Molecular simulations rationalize why this rearrangement was only observed for the prismatic [3 + 6] cage TCC1 but not for the analogous [3 + 6] cages, TCC2 and TCC3. Solvent was found to be a dominant factor in driving this rearrangement.
Greenaway RL, Holden D, Eden EGB, et al., 2017, Understanding gas capacity, guest selectivity, and diffusion in porous liquids, Chemical Science, Vol: 8, Pages: 2640-2651, ISSN: 2041-6520
Porous liquids are a new class of material that could have applications in areas such as gas separation and homogeneous catalysis. Here we use a combination of measurement techniques, molecular simulations, and control experiments to advance the quantitative understanding of these liquids. In particular, we show that the cage cavities remain unoccupied in the absence of a suitable guest, and that the liquids can adsorb large quantities of gas, with gas occupancy in the cages as high as 72% and 74% for Xe and SF6, respectively. Gases can be reversibly loaded and released by using non-chemical triggers such as sonication, suggesting potential for gas separation schemes. Diffusion NMR experiments show that gases are in dynamic equilibrium between a bound and unbound state in the cage cavities, in agreement with recent simulations for related porous liquids. Comparison with gas adsorption in porous organic cage solids suggests that porous liquids have similar gas binding affinities, and that the physical properties of the cage molecule are translated into the liquid state. By contrast, some physical properties are different: for example, solid homochiral porous cages show enantioselectivity for chiral aromatic alcohols, whereas the equivalent homochiral porous liquids do not. This can be attributed to a loss of supramolecular organisation in the isotropic porous liquid.
Chintalapudi V, Galvin EA, Greenaway RL, et al., 2016, Combining cycloisomerization with trienamine catalysis: a regiochemically flexible enantio- and diastereoselective synthesis of hexahydroindoles, CHEMICAL COMMUNICATIONS, Vol: 52, Pages: 693-696, ISSN: 1359-7345
Porous solids such as zeolites1 and metal–organic frameworks2,3 are useful in molecular separation and in catalysis, but their solid nature can impose limitations. For example, liquid solvents, rather than porous solids, are the most mature technology for post-combustion capture of carbon dioxide because liquid circulation systems are more easily retrofitted to existing plants. Solid porous adsorbents offer major benefits, such as lower energy penalties in adsorption–desorption cycles4, but they are difficult to implement in conventional flow processes. Materials that combine the properties of fluidity and permanent porosity could therefore offer technological advantages, but permanent porosity is not associated with conventional liquids5. Here we report free-flowing liquids whose bulk properties are determined by their permanent porosity. To achieve this, we designed cage molecules6,7 that provide a well-defined pore space and that are highly soluble in solvents whose molecules are too large to enter the pores. The concentration of unoccupied cages can thus be around 500 times greater than in other molecular solutions that contain cavities8,9,10, resulting in a marked change in bulk properties, such as an eightfold increase in the solubility of methane gas. Our results provide the basis for development of a new class of functional porous materials for chemical processes, and we present a one-step, multigram scale-up route for highly soluble ‘scrambled’ porous cages prepared from a mixture of commercially available reagents. The unifying design principle for these materials is the avoidance of functional groups that can penetrate into the molecular cage cavities.
Campbell CD, Greenaway RL, Holton OT, et al., 2015, Ynamide Carbopalladation: A Flexible Route to Mono-, Bi- and Tricyclic Azacycles, CHEMISTRY-A EUROPEAN JOURNAL, Vol: 21, Pages: 12627-12639, ISSN: 0947-6539
Campbell CD, Greenaway RL, Holton OT, et al., 2014, Palladium-catalyzed cyclization of bromoenynamides to tricyclic azacycles: synthesis of trikentrin-like frameworks, CHEMICAL COMMUNICATIONS, Vol: 50, Pages: 5187-5189, ISSN: 1359-7345
Greenaway RL, Campbell CD, Chapman HA, et al., 2012, Reductive Cyclization of Bromoenynamides with Alcohols as Hydride Source: Synthesis and Reactions of 2-Amidodienes, ADVANCED SYNTHESIS & CATALYSIS, Vol: 354, Pages: 3187-3194, ISSN: 1615-4150
Greenaway RL, Campbell CD, Holton OT, et al., 2011, Palladium-Catalyzed Cascade Cyclization of Ynamides to Azabicycles, CHEMISTRY-A EUROPEAN JOURNAL, Vol: 17, Pages: 14366-14370, ISSN: 0947-6539
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