Imperial College London

DrKimJelfs

Faculty of Natural SciencesDepartment of Chemistry

Reader in Computational Materials Chemistry
 
 
 
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Contact

 

+44 (0)20 7594 3438k.jelfs Website

 
 
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Location

 

207AMolecular Sciences Research HubWhite City Campus

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Summary

 

Publications

Publication Type
Year
to

109 results found

Bennett S, Szczypiński FT, Turcani L, Briggs ME, Greenaway RL, Jelfs KEet al., 2021, Materials Precursor Score: Modeling Chemists' Intuition for the Synthetic Accessibility of Porous Organic Cage Precursors., J Chem Inf Model

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.

Journal article

Tarzia A, Lewis JEM, Jelfs KE, 2021, High‐Throughput Computational Evaluation of Low Symmetry Pd 2 L 4 Cages to Aid in System Design**, Angewandte Chemie, ISSN: 0044-8249

Journal article

Reger D, Haines P, Amsharov KY, Schmidt JA, Ullrich T, Bönisch S, Hampel F, Görling A, Nelson J, Jelfs KE, Guldi DM, Jux Net al., 2021, A Family of Superhelicenes: Easily Tunable, Chiral Nanographenes by Merging Helicity with Planar π Systems, Angewandte Chemie, Vol: 133, Pages: 18221-18229, ISSN: 0044-8249

Journal article

Jux N, Reger D, Haines P, Amsharov KY, Schmidt JA, Ullrich T, Bönisch S, Hampel F, Görling A, Nelson J, Jelfs KE, Guldi DMet al., 2021, A family of superhelicenes - easily tunable, chiral nanographenes by merging helicity with planar π-systems, Angewandte Chemie International Edition, Vol: 60, Pages: 18073-18081, ISSN: 1433-7851

Incorporating helicity into large polycyclic aromatic hydrocarbons (PAHs) constitutes a new field of research at the interface between chemistry and material sciences. Lately, interest in the design of π-extended helicenes has surged. This new class of twisted, chiral nanographenes not only reveals unique characteristics but also finds its way into emerging applications such as spintronics. Insights into their structure-property relationships and on-demand tuning are scarce. To close these knowledge gaps, we designed a straightforward synthetic route towards a full-fledged family of π-extended helicenes: superhelicenes. Common are two hexa-peri-hexabenzocoronenes (HBCs) connected via a central 5-membered ring. By means of structurally altering this 5-membered ring, we realized a versatile library of molecular building blocks. Not only the superhelicene structure, but also their features are tuned with ease. In-depth physico-chemical characterizations served as a proof of concept thereof. The superhelicene enantiomers were separated, their circular dichroism was measured in preliminary studies and concluded with an enantiomeric assignment. Our work was rounded-off by crystal structure analyses. Mixed stacks of M- and P-isomers led to twisted molecular wires. Using such stacks, charge-carrier mobilities were calculated, giving reason to expect outstanding hole transporting properties.

Journal article

Schmidt J, Weatherby J, Sugden I, Santana-Bonilla A, Salerno F, Fuchter M, Johnson E, Nelson J, Jelfs Ket al., 2021, Computational screening of organic semiconductors: exploring side-group functionalisation and assembly to optimise charge transport in chiral molecules, Crystal Growth and Design, ISSN: 1528-7483

Molecular materials are challenging to design as their packing arrangement and hence properties are subject to subtle variations in the interplay of soft intermolecular interactions that are difficult to predict. The rational design of new molecular materials with tailored properties is currently hampered by the lack of knowledge of how a candidate molecule will pack in space and how we can control the polymorphs we can experimentally obtain. Here, we develop a simplified approach to aid the material design process, by the development of a screening process that is used to test 1344 helicene molecules that have potential as organic electronic materials. Our approach bridges the gap between single molecule design, molecular assembly, and the resulting charge-carrier mobilities. We find that fluorination significantly improves electron transport in the molecular material by up to 200%; the reference [6]helicene packing showed a mobility of 0.30 cm2 V-1 s-1, fluorination increased the mobility to up to 0.96 and 0.97 (13-fluoro[6]H and 4,13-difluoro[6]H), assuming an outer reorganisation energy of 0.30 eV. Side groups containing triple bonds largely lead to improved transfer integrals. We validate our screening approach through the use of crystal structure prediction to confirm the presence of favourable packing motifs to maximize charge mobility.

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, 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

Tarzia A, Lewis J, Jelfs KE, 2021, High‐throughput computational evaluation of low symmetry Pd2L4 cages to aid in system design, Angewandte Chemie International Edition, ISSN: 1433-7851

The use of unsymmetrical components in metallo-supramolecular chemistry allows for low- symmetry architectures with anisotropic cavities toward guest-binding with high specificity and affinity. Unsymmetrical ditopic ligands mixed with Pd(II) have the potential to self-assemble into reduced symmetry Pd 2 L 4 metallo-architectures. Mixtures of isomers can form, however, resulting in potentially undesirable heterogeneity within a system. Therefore it is paramount to be able to design components that preferentially form a single isomer. Previous data suggested that computational methods could predict with reasonable accuracy whether unsymmetrical ligands would preferentially self-assemble into a single isomer under constraints of geometrical mismatch. We successfully apply a collaborative computational and experimental workflow to mitigate costly trial-and-error synthetic approaches. Our low-cost computational workflow rapidly constructs new unsymmetrical ligands (and Pd 2 L 4 cage isomers) and ranks their likelihood for forming cis -Pd 2 L 4 assemblies. From this narrowed search space, we successfully synthesised four new low-symmetry, cis -Pd 2 L 4 cages, with cavities of different shapes and sizes.

Journal article

Zou Y-Q, Zhang D, Ronson TK, Tarzia A, Lu Z, Jelfs KE, Nitschke JRet al., 2021, Sterics and Hydrogen Bonding Control Stereochemistry and Self-Sorting in BINOL-Based Assemblies, JOURNAL OF THE AMERICAN CHEMICAL SOCIETY, Vol: 143, Pages: 9009-9015, ISSN: 0002-7863

Journal article

Turcani L, Tarzia A, Szczypinski F, Jelfs Ket al., 2021, stk: an extendable Python framework for automated molecular and supramolecular structure assembly and discovery, Journal of Chemical Physics, Vol: 154, ISSN: 0021-9606

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

Journal article

Yuan Q, Longo M, Thornton A, McKeown NB, Comesana-Gandara B, Jansen JC, Jelfs Ket al., 2021, Imputation of missing gas permeability data for polymer membranes using machine learning, Journal of Membrane Science, Vol: 627, Pages: 1-10, ISSN: 0376-7388

Polymer-based membranes have the potential for use in energy efficient gas separations. The successful exploitation of new materials requires accurate knowledge of the transport properties of all gases of interest. Open-source databases of gas permeabilities are of significant potential benefit to the research community. The Membrane Society of Australasia (https://membrane-australasia.org/) hosts a database for experimentally measured and reported polymer gas permeabilities. However, the database is incomplete, limiting its potential use as a research tool. Here, missing values in the database were imputed (filled) using machine learning (ML). The ML model was validated against gas permeability measurements that were not recorded in the database. Through imputing the missing data, it is possible to re-analyse historical polymers and look for potential “missed” candidates with promising gas selectivity. In addition, for systems with limited experimental data, ML using sparse features was performed, and we suggest that once the permeability of CO2 and/or O2 for a polymer has been measured, most other gas permeabilities and selectivities, including those for CO2/CH4 and CO2/N2, can be quantitatively estimated. This early insight into the gas permeability of a new system can be used at an initial stage of experimental measurements to rapidly identify polymer membranes worth further investigation.

Journal article

Peach R, Arnaudon A, Schmidt J, Palasciano HA, Bernier NR, Jelfs K, Yaliraki S, Barahona Met al., 2021, HCGA: Highly comparative graph analysis for network phenotyping, Patterns, Vol: 2, ISSN: 2666-3899

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

Journal article

Sapnik AF, Bechis I, Collins SM, Johnstone DN, Divitini G, Smith AJ, Chater PA, Addicoat MA, Johnson T, Keen DA, Jelfs KE, Bennett TDet al., 2021, Mixed hierarchical local structure in a disordered metal-organic framework, NATURE COMMUNICATIONS, Vol: 12, ISSN: 2041-1723

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

Heath-Apostolopoulos I, Vargas-Ortiz D, Wilbraham L, Jelfs KE, Zwijnenburg MAet al., 2021, Using high-throughput virtual screening to explore the optoelectronic property space of organic dyes; finding diketopyrrolopyrrole dyes for dye-sensitized water splitting and solar cells (vol 5, pg 704, 2021), SUSTAINABLE ENERGY & FUELS, Vol: 5, Pages: 1584-1584, ISSN: 2398-4902

Journal article

Heath-Apostolopoulos I, Vargas-Ortiz D, Wilbraham L, Jelfs KE, Zwijnenburg MAet al., 2021, Using high-throughput virtual screening to explore the optoelectronic property space of organic dyes; finding diketopyrrolopyrrole dyes for dye-sensitized water splitting and solar cells, SUSTAINABLE ENERGY & FUELS, Vol: 5, Pages: 704-719, ISSN: 2398-4902

Journal article

Szczypinski FT, Bennett S, Jelfs KE, 2021, Can we predict materials that can be synthesised?, CHEMICAL SCIENCE, Vol: 12, Pages: 830-840, ISSN: 2041-6520

Journal article

Shi W, Salerno F, Ward MD, Santana-Bonilla A, Wade J, Hou X, Liu T, Dennis TJS, Campbell AJ, Jelfs KE, Fuchter MJet al., 2021, Fullerene desymmetrization as a means to achieve single-enantiomer electron acceptors with maximized chiroptical responsiveness., Advanced Materials, Vol: 33, Pages: 1-7, ISSN: 0935-9648

Solubilized fullerene derivatives have revolutionized the development of organic photovoltaic devices, acting as excellent electron acceptors. The addition of solubilizing addends to the fullerene cage results in a large number of isomers, which are generally employed as isomeric mixtures. Moreover, a significant number of these isomers are chiral, which further adds to the isomeric complexity. The opportunities presented by single-isomer, and particularly single-enantiomer, fullerenes in organic electronic materials and devices are poorly understood however. Here, ten pairs of enantiomers are separated from the 19 structural isomers of bis[60]phenyl-C61-butyric acid methyl ester, using them to elucidate important chiroptical relationships and demonstrating their application to a circularly polarized light (CPL)-detecting device. Larger chiroptical responses are found, occurring through the inherent chirality of the fullerene. When used in a single-enantiomer organic field-effect transistor, the potential to discriminate CPL with a fast light response time and with a very high photocurrent dissymmetry factor (gph  = 1.27 ± 0.06) is demonstrated. This study thus provides key strategies to design fullerenes with large chiroptical responses for use as chiral components of organic electronic devices. It is anticipated that this data will position chiral fullerenes as an exciting material class for the growing field of chiral electronic technologies.

Journal article

Abet V, Szczypinski FT, Little MA, Santolini V, Jones CD, Evans R, Wilson C, Wu X, Thorne MF, Bennison MJ, Cui P, Cooper AI, Jelfs KE, Slater AGet al., 2020, Inducing Social Self-Sorting in Organic Cages To Tune The Shape of The Internal Cavity (vol 59, pg 16755, 2020), ANGEWANDTE CHEMIE-INTERNATIONAL EDITION, Vol: 59, Pages: 20272-20272, ISSN: 1433-7851

Journal article

Abet V, Szczypiński FT, Little MA, Santolini V, Jones CD, Evans R, Wilson C, Wu X, Thorne MF, Bennison MJ, Cui P, Cooper AI, Jelfs KE, Slater AGet 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.

Journal article

Abet V, Szczypiński FT, Little MA, Santolini V, Jones CD, Evans R, Wilson C, Wu X, Thorne MF, Bennison MJ, Cui P, Cooper AI, Jelfs KE, Slater AGet 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.

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

Thompson KA, Mathias R, Kim D, Kim J, Rangnekar N, Johnson JR, Hoy SJ, Bechis I, Tarzia A, Jelfs KE, McCool BA, Livingston A, Lively RP, Finn MGet al., 2020, N-Aryl-linked spirocyclic polymers for membrane separations of complex hydrocarbon mixtures, Science, 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.

Journal article

Eder S, Yoo D-J, Nogala W, Pletzer M, Santana Bonilla A, White AJP, Jelfs KE, Heeney M, Choi JW, Glöcklhofer Fet 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, [2.2.2.2]paracyclophane-1,9,17,25-tetraene (PCT), can stabilize the neutral state by local aromaticity and the doubly reduced state by global aromaticity, resulting in an anode material with extraordinarily stable cycling performance and outstanding performance under fast-charge/discharge conditions, demonstrating an exciting new path for the development of electrode materials for SIBs and other types of batteries.

Journal article

Schmidt J, Weatherby JA, Sugden I, Santana-Bonilla A, Salerno F, Fuchter M, Johnson E, Nelson J, Jelfs Ket al., 2020, Computational Screening of Organic Semiconductors: Exploring Side-Group Functionalisation and Assembly to Optimise Charge Transport in Chiral Molecules, Publisher: American Chemical Society (ACS)

<jats:p>&lt;p&gt;Molecular materials are challenging to design as their packing arrangement and hence properties are subject to subtle variations in the interplay of soft intermolecular interactions that are difficult to predict. The rational design of new molecular materials with tailored properties is currently hampered by the lack of knowledge of how a candidate molecule will pack in space and how we can control the polymorphs we can experimentally obtain. Here, we develop a simplified approach to aid the material design process, by the development of a screening process that is used to test 1344 helicene molecules that have potential as organic electronic materials. Our approach bridges the gap between single molecule design, molecular assembly, and the resulting charge-carrier mobilities. We find that fluorination significantly improves electron transport in the molecular material by up to 200%; the reference [6]helicene packing showed a mobility of 0.30 cm2 V-1 s-1, fluorination increased the mobility to up to 0.96 and 0.97 (13-fluoro[6]H and 4,13-difluoro[6]H), assuming an outer reorganisation energy of 0.30 eV. Side groups containing triple bonds largely lead to improved transfer integrals. We validate our screening approach through the use of crystal structure prediction to confirm the presence of favourable packing motifs to maximize charge mobility.&lt;/p&gt;</jats:p>

Working paper

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

Yuan Q, Santana Bonilla A, Zwijnenburg MA, Jelfs Ket 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.

Journal article

Tan R, Wang A, Malpass-Evans R, Williams R, Zhao EW, Liu T, Ye C, Zhou X, Darwich BP, Fan Z, Turcani L, Jackson E, Chen L, Chong SY, Li T, Jelfs KE, Cooper AI, Brandon NP, Grey CP, McKeown NB, Song Qet 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.

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

Bennett S, Tarzia A, Zwijnenburg MA, Jelfs KEet al., 2020, Chapter 12: Artificial Intelligence Applied to the Prediction of Organic Materials, RSC Theoretical and Computational Chemistry Series, Pages: 280-310

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.

Book chapter

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

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