79 results found
Greenaway RL, Santolini V, Pulido A, et al., 2019, From Concept to Crystals via Prediction: Multi-Component Organic Cage Pots by Social Self-Sorting, ANGEWANDTE CHEMIE-INTERNATIONAL EDITION, ISSN: 1433-7851
Greenaway RL, Santolini V, Pulido A, et al., From Concept to Crystals via Prediction: Multi‐Component Organic Cage Pots by Social Self‐Sorting, Angewandte Chemie, ISSN: 0044-8249
Jackson E, Miklitz M, Song Q, et al., 2019, Computational Evaluation of the Diffusion Mechanisms for C8 Aromatics in Porous Organic Cages, JOURNAL OF PHYSICAL CHEMISTRY C, Vol: 123, Pages: 21011-21021, ISSN: 1932-7447
Berardo E, Miklitz M, Greenaway R, et al., Computational screening for nested organic cage complexes, Molecular Systems Design and Engineering, 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.
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.
Sprick RS, Aitchison CM, Berardo E, et al., 2018, Maximising the hydrogen evolution activity in organic photocatalysts by co-polymerisation, JOURNAL OF MATERIALS CHEMISTRY A, Vol: 6, Pages: 11994-12003, ISSN: 2050-7488
Wilbraham L, Berardo E, Turcani L, et al., 2018, A high-throughput screening approach for the optoelectronic properties of conjugated polymers, Journal of Chemical Information and Modeling, Vol: 58, Pages: 2450-2459, ISSN: 1549-9596
We propose a general high-throughput virtual screening approach for the optical and electronic properties of conjugated polymers. This approach makes use of the recently developed xTB family of low-computational-cost density functional tight-binding methods from Grimme and co-workers, calibrated here to (TD-)DFT data computed for a representative diverse set of (co-)polymers. Parameters drawn from the resulting calibration using a linear model can then be applied to the xTB derived results for new polymers, thus generating near DFT-quality data with orders of magnitude reduction in computational cost. As a result, after an initial computational investment for calibration, this approach can be used to quickly and accurately screen on the order of thousands of polymers for target applications. We also demonstrate that the (opto)electronic properties of the conjugated polymers show only a very minor variation when considering different conformers and that the results of high-throughput screening are therefore expected to be relatively insensitive with respect to the conformer search methodology applied.
Yang Y, Rice B, Shi X, et al., 2018, Emergent Properties of an Organic Semiconductor Driven by its Molecular Chirality (vol 11, pg 8329, 2017), ACS NANO, Vol: 12, Pages: 6343-6343, ISSN: 1936-0851
Rice B, LeBlanc LM, Otero-de-la-Roza A, et al., 2018, A computational exploration of the crystal energy and charge-carrier mobility landscapes of the chiral helicene molecule (vol 10, pg 1865, 2018), NANOSCALE, Vol: 10, Pages: 9410-9410, ISSN: 2040-3364
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 & DESIGN, Vol: 18, Pages: 2759-2764, ISSN: 1528-7483
Meier C, Turkani L, Sprick R, et al., 2018, Integrated experimental and computational high-throughput screening for polymer photocatalysts, 255th National Meeting and Exposition of the American-Chemical-Society (ACS) - Nexus of Food, Energy, and Water, Publisher: AMER CHEMICAL SOC, ISSN: 0065-7727
Slater AG, Little MA, Briggs ME, et al., 2018, A solution-processable dissymmetric porous organic cage, MOLECULAR SYSTEMS DESIGN & ENGINEERING, Vol: 3, Pages: 223-227, ISSN: 2058-9689
Rice B, LeBlanc LM, Otero-de-la-Roza A, et al., 2018, A computational exploration of the crystal energy and charge-carrier mobility landscapes of the chiral helicene molecule, Nanoscale, Vol: 10, Pages: 1865-1876, ISSN: 2040-3364
The potential of a given π-conjugated organic molecule in an organic semiconductor device is highly dependent on molecular packing, as it strongly influences the charge-carrier mobility of the material. Such solid-state packing is sensitive to subtle differences in their intermolecular interactions and is challenging to predict. Chirality of the organic molecule adds an additional element of complexity to intuitive packing prediction. Here we use crystal structure prediction to explore the lattice-energy landscape of a potential chiral organic semiconductor, helicene. We reproduce the experimentally observed enantiopure crystal structure and explain the absence of an experimentally observed racemate structure. By exploring how the hole and electron-mobility varies across the energy–structure–function landscape for helicene, we find that an energetically favourable and frequently occurring packing motif is particularly promising for electron-mobility, with a highest calculated mobility of 2.9 cm2 V−1 s−1 (assuming a reorganization energy of 0.46 eV). We also calculate relatively high hole-mobility in some structures, with a highest calculated mobility of 2.0 cm2 V−1 s−1 found for chains of helicenes packed in a herringbone fashion. Neither the energetically favourable nor high charge-carrier mobility packing motifs are intuitively obvious, and this demonstrates the utility of our approach to computationally explore the energy–structure–function landscape for organic semiconductors. Our work demonstrates a route for the use of computational simulations to aid in the design of new molecules for organic electronics, through the a priori prediction of their likely solid-state form and properties.
Yang Y, Rice B, Shi X, et al., 2017, Emergent Properties of an Organic Semiconductor Driven by its Molecular Chirality, ACS Nano, Vol: 11, Pages: 8329-8338, ISSN: 1936-0851
Chiral molecules exist as pairs of nonsuperimposable mirror images; a fundamental symmetry property vastly underexplored in organic electronic devices. Here, we show that organic field-effect transistors (OFETs) made from the helically chiral molecule 1-azahelicene can display up to an 80-fold difference in hole mobility, together with differences in thin-film photophysics and morphology, solely depending on whether a single handedness or a 1:1 mixture of left- and right-handed molecules is employed under analogous fabrication conditions. As the molecular properties of either mirror image isomer are identical, these changes must be a result of the different bulk packing induced by chiral composition. Such underlying structures are investigated using crystal structure prediction, a computational methodology rarely applied to molecular materials, and linked to the difference in charge transport. These results illustrate that chirality may be used as a key tuning parameter in future device applications.
Miklitz M, Jiang S, Clowes R, et al., 2017, Computational screening of porous organic molecules for xenon/krypton separation, Journal of Physical Chemistry C, Vol: 121, Pages: 15211-15222, ISSN: 1932-7447
We performed a computational screening of previously reported porous molecular materials, including porous organic cages, cucurbiturils, cyclodextrins, and cryptophanes, for Xe/Kr separation. Our approach for rapid screening through analysis of single host molecules, rather than the solid state structure of the materials, is evaluated. We use a set of tools including in-house software for structural evaluations, electronic structure calculations for guest binding energies, and molecular dynamics and metadynamics simulations to study the effect of the hosts’ flexibility upon guest diffusion. Our final results confirm that the CC3 cage molecule, previously reported as high performing for Xe/Kr separation, is the most promising of this class of materials reported to date. The Noria molecule was also found to be promising, and we therefore synthesized two related Noria molecules and tested their performance for Xe/Kr separation in the laboratory.
Hasell T, Little MA, Chong SY, et al., 2017, Chirality as a tool for function in porous organic cages, Nanoscale, Vol: 9, Pages: 6783-6790, ISSN: 2040-3364
The control of solid state assembly for porous organic cages is more challenging than for extended frameworks, such as metal–organic frameworks. Chiral recognition is one approach to achieving this control. Here we investigate chiral analogues of cages that were previously studied as racemates. We show that chiral cages can be produced directly from chiral precursors or by separating racemic cages by co-crystallisation with a second chiral cage, opening up a route to producing chiral cages from achiral precursors. These chiral cages can be cocrystallized in a modular, ‘isoreticular’ fashion, thus modifying porosity, although some chiral pairings require a specific solvent to direct the crystal into the desired packing mode. Certain cages are shown to interconvert chirality in solution, and the steric factors governing this behavior are explored both by experiment and by computational modelling.
Evans JD, Jelfs KE, Day GM, et al., 2017, Application of computational methods to the design and characterisation of porous molecular materials, Chemical Society Reviews, Vol: 46, Pages: 3286-3301, ISSN: 1460-4744
Composed from discrete units, porous molecular materials (PMMs) possess unique properties not observed for conventional, extended, solids, such as solution processibility and permanent porosity in the liquid phase. However, identifying the origin of porosity is not a trivial process, especially for amorphous or liquid phases. Furthermore, the assembly of molecular components is typically governed by a subtle balance of weak intermolecular forces that makes structure prediction challenging. Accordingly, in this review we canvass the crucial role of molecular simulations in the characterisation and design of PMMs. We will outline strategies for modelling porosity in crystalline, amorphous and liquid phases and also describe the state-of-the-art methods used for high-throughput screening of large datasets to identify materials that exhibit novel performance characteristics.
We define a nomenclature for the classification of porous organic cage molecules, enumerating the 20 most probable topologies, 12 of which have been synthetically realised to date. We then discuss the computational challenges encountered when trying to predict the most likely topological outcomes from dynamic covalent chemistry (DCC) reactions of organic building blocks. This allows us to explore the extent to which comparing the internal energies of possible reaction outcomes is successful in predicting the topology for a series of 10 different building block combinations.
Song Q, Liu TY, Jelfs KE, et al., Advanced Microporous Membranes for Molecular Separations, British Zeolite Association 40th Annual Meeting
Slater AG, Little MA, Pulido A, et al., 2016, Reticular synthesis of porous molecular 1D nanotubes and 3D networks, Nature Chemistry, Vol: 9, Pages: 17-25, ISSN: 1755-4349
Synthetic control over pore size and pore connectivity is the crowning achievement for porous metal-organic frameworks (MOFs). The same level of control has not been achieved for molecular crystals, which are not defined by strong, directional intermolecular coordination bonds. Hence, molecular crystallization is inherently less controllable than framework crystallization, and there are fewer examples of 'reticular synthesis', in which multiple building blocks can be assembled according to a common assembly motif. Here we apply a chiral recognition strategy to a new family of tubular covalent cages to create both 1D porous nanotubes and 3D diamondoid pillared porous networks. The diamondoid networks are analogous to MOFs prepared from tetrahedral metal nodes and linear ditopic organic linkers. The crystal structures can be rationalized by computational lattice-energy searches, which provide an in silico screening method to evaluate candidate molecular building blocks. These results are a blueprint for applying the 'node and strut' principles of reticular synthesis to molecular crystals.
This data is extracted from the Web of Science and reproduced under a licence from Thomson Reuters. You may not copy or re-distribute this data in whole or in part without the written consent of the Science business of Thomson Reuters.