Publications
172 results found
Ye C, Wang A, Breakwell C, et al., 2022, Development of efficient aqueous organic redox flow batteries using ion-sieving sulfonated polymer membranes, Nature Communications, Vol: 13, ISSN: 2041-1723
Redox flow batteries using aqueous organic-based electrolytes are promising candidates for developing cost-effective grid-scale energy storage devices. However, a significant drawback of these batteries is the cross-mixing of active species through the membrane, which causes battery performance degradation. To overcome this issue, here we report size-selective ion-exchange membranes prepared by sulfonation of a spirobifluorene-based microporous polymer and demonstrate their efficient ion sieving functions in flow batteries. The spirobifluorene unit allows control over the degree of sulfonation to optimize the transport of cations, whilst the microporous structure inhibits the crossover of organic molecules via molecular sieving. Furthermore, the enhanced membrane selectivity mitigates the crossover-induced capacity decay whilst maintaining good ionic conductivity for aqueous electrolyte solution at pH 9, where the redox-active organic molecules show long-term stability. We also prove the boosting effect of the membranes on the energy efficiency and peak power density of the aqueous redox flow battery, which shows stable operation for about 120 h (i.e., 2100 charge-discharge cycles at 100 mA cm−2) in a laboratory-scale cell.
Bechis I, Sapnik A, Tarzia A, et al., 2022, Modelling the effect of defects and disorder in amorphous metal−organic frameworks
<jats:p>Amorphous metal−organic frameworks (aMOFs) are a class of disordered framework materials with a defined local order given by the connectivity between inorganic nodes and organic linkers, but absent longer-range order. The rational development of function for aMOFs is hindered by our limited understanding of the underlying structure-property relationships in these systems, a consequence of the absence of long-range order, which makes experimental characterization particularly challenging. Here, we use a versatile modelling approach to generate in-silico structural models for an aMOF based on Fe trimers and 1,3,5-benzenetricarboxylate (BTC) linkers, Fe-BTC. We build a phase space for this material that includes nine amorphous phases with different degrees of defects and local order. These models are analyzed through a combination of structural analysis, pore analysis and pair distribution functions. Therefore, we are able to systematically explore the effects of the variation of each of these features, both in isolation and combined, for a disordered MOF system, something that would not be possible through experiment alone. We find that the degree of local order has a greater impact on structure and properties than the degree of defects. The approach presented here is versatile and allows for the study of different structural features and MOF chemistries, enabling the development of design rules for the rational design of aMOFs.</jats:p>
Egleston BD, Mroz A, Jelfs KE, et 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.
Sapnik AF, Bechis I, Bumstead AM, et al., 2022, Multivariate analysis of disorder in metal-organic frameworks, NATURE COMMUNICATIONS, Vol: 13
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- Citations: 5
Yuan Q, Szczypiński FT, Jelfs KE, 2022, Explainable graph neural networks for organic cages., Digit Discov, Vol: 1, Pages: 127-138
The development of accurate and explicable machine learning models to predict the properties of topologically complex systems is a challenge in materials science. Porous organic cages, a class of polycyclic molecular materials, have potential application in molecular separations, catalysis and encapsulation. For most applications of porous organic cages, having a permanent internal cavity in the absence of solvent, a property termed "shape persistence" is critical. Here, we report the development of Graph Neural Networks (GNNs) to predict the shape persistence of organic cages. Graph neural networks are a class of neural networks where the data, in our case that of organic cages, are represented by graphs. The performance of the GNN models was measured against a previously reported computational database of organic cages formed through a range of [4 + 6] reactions with a variety of reaction chemistries. The reported GNNs have an improved prediction accuracy and transferability compared to random forest predictions. Apart from the improvement in predictive power, we explored the explicability of the GNNs by computing the integrated gradient of the GNN input. The contribution of monomers and molecular fragments to the shape persistence of the organic cages could be quantitatively evaluated with integrated gradients. With the added explicability of the GNNs, it was possible not only to accurately predict the property of organic materials, but also to interpret the predictions of the deep learning models and provide structural insights for the discovery of future materials.
Tarzia A, Jelfs K, 2022, Unlocking the computational design of metal-organic cages, Chemical Communications, Vol: 58, Pages: 3717-3730, ISSN: 1359-7345
Metal–organic cages are macrocyclic structures that can possess an intrinsic void that can hold molecules for encapsulation, adsorption, sensing, and catalysis applications. As metal–organic cages may be comprised from nearly any combination of organic and metal-containing components, cages can form with diverse shapes and sizes, allowing for tuning toward targeted properties. Therefore, their near-infinite design space is almost impossible to explore through experimentation alone and computational design can play a crucial role in exploring new systems. Although high-throughput computational design and screening workflows have long been known as powerful tools in drug and materials discovery, their application in exploring metal–organic cages is more recent. We show examples of structure prediction and host–guest/catalytic property evaluation of metal–organic cages. These examples are facilitated by advances in methods that handle metal-containing systems with improved accuracy and are the beginning of the development of automated cage design workflows. We finally outline a scope for how high-throughput computational methods can assist and drive experimental decisions as the field pushes toward functional and complex metal–organic cages. In particular, we highlight the importance of considering realistic, flexible systems.
Ning G-H, Cui P, Sazanovich I, et al., 2021, Organic cage inclusion crystals exhibiting guest-enhanced multiphoton harvesting, CHEM, Vol: 7, Pages: 3157-3170, ISSN: 2451-9294
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- Citations: 6
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, 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.
Kai A, Egleston BD, Tarzia A, et 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.
Tarzia A, Lewis JEM, Jelfs KE, 2021, High‐throughput computational evaluation of low symmetry Pd <sub>2</sub> L <sub>4</sub> Cages to Aid in System Design**, Angewandte Chemie, Vol: 133, Pages: 21047-21055, ISSN: 0044-8249
Unsymmetrical ditopic ligands can self-assemble into reduced-symmetry Pd2L4 metallo-cages with anisotropic cavities, with implications for high specificity and affinity guest-binding. Mixtures of cage isomers can form, however, resulting in undesirable system heterogeneity. 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 single cage isomers under constraints of geometrical mismatch. We successfully apply a collaborative computational and experimental workflow to mitigate costly trial-and-error synthetic approaches. Our rapid computational workflow constructs unsymmetrical ligands and their Pd2L4 cage isomers, ranking the likelihood for exclusively forming cis-Pd2L4 assemblies. From this narrowed search space, we successfully synthesised four new, low-symmetry, cis-Pd2L4 cages.
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, Vol: 60, Pages: 20879-20887, 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.
Schmidt J, Weatherby J, Sugden I, et al., 2021, Computational screening of organic semiconductors: exploring side-group functionalisation and assembly to optimise charge transport in chiral molecules, Crystal Growth and Design, Vol: 21, Pages: 5036-5049, 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.
Reger D, Haines P, Amsharov KY, et 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
<jats:title>Abstract</jats:title><jats:p>We designed a straightforward synthetic route towards a full‐fledged family of π‐extended helicenes: superhelicenes. They have two hexa‐peri‐hexabenzocoronenes (HBCs) in common that are connected via a central five‐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.</jats:p>
Jux N, Reger D, Haines P, et 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.
Wolpert EH, Jelfs KE, 2021, Predicting the packing behaviour of porous organic cages, Publisher: INT UNION CRYSTALLOGRAPHY, Pages: C712-C712, ISSN: 2053-2733
Bennett S, Szczypiński F, Turcani L, et al., 2021, Materials Precursor Score: Modelling Chemists' Intuition for the Synthetic Accessibility of Porous Organic Cage Precursors
<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>
Zou Y-Q, Zhang D, Ronson TK, et 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
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- Citations: 24
Turcani L, Tarzia A, Szczypinski F, et 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.
Yuan Q, Longo M, Thornton A, et 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.
Turcani L, Tarzia A, Szczypiński F, et al., 2021, Stk: An Extendable Python Framework for Automated Molecular and Supramolecular Structure Assembly and Discovery
<jats:p>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, <jats:italic>stk</jats:italic>, an open-source, extendable and modular Python framework for general structure generation of (supra)molecular structures. Our construction approach follows a bottom-up process and minimises the input required from the user, making <jats:italic>stk</jats:italic> user-friendly and applicable to many material classes. This version of <jats:italic>stk</jats:italic> 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 visualisation. The latest version of <jats:italic>stk</jats:italic> is freely available at github.com/lukasturcani/stk</jats:p>
Peach R, Arnaudon A, Schmidt J, et 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.
Sapnik AF, Bechis I, Collins SM, et al., 2021, Mixed hierarchical local structure in a disordered metal-organic framework, NATURE COMMUNICATIONS, Vol: 12, ISSN: 2041-1723
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- Citations: 33
Bennett S, Szczypiński F, Turcani L, et 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>
Schmidt JA, Weatherby JA, Sugden I, et al., 2021, Computational Screening of Organic Semiconductors: Exploring Side-Group Functionalisation and Assembly to Optimise Charge Transport in Chiral Molecules
<jats:p>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.</jats:p>
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.
Turcani L, Tarzia A, Szczypiński F, et al., 2021, Stk: An Extendable Python Framework for Automated Molecular and Supramolecular Structure Assembly and Discovery
<jats:p>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, <jats:italic>stk</jats:italic>, an open-source, extendable and modular Python framework for general structure generation of (supra)molecular structures. Our construction approach follows a bottom-up process and minimises the input required from the user, making <jats:italic>stk</jats:italic> user-friendly and applicable to many material classes. This version of <jats:italic>stk</jats:italic> 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 visualisation. The latest version of <jats:italic>stk</jats:italic> is freely available at github.com/lukasturcani/stk</jats:p>
Heath-Apostolopoulos I, Vargas-Ortiz D, Wilbraham L, et 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
Yuan Q, Longo M, Thornton A, et al., 2021, Imputation of Missing Gas Permeability Data for Polymer Membranes using Machine Learning
<jats:p>Polymer-based membranes can be used for energy efficient gas separations. Successful exploitation of new materials requires accurate knowledge of the transport properties of all gases of interest. An open source database of such data is of significant 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 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 CO<jats:sub>2</jats:sub> and/or O<jats:sub>2</jats:sub> for a polymer has been measured, most other gas permeabilities and selectivities, including those for CO<jats:sub>2</jats:sub>/CH<jats:sub>4</jats:sub> and CO<jats:sub>2</jats:sub>/N<jats:sub>2</jats:sub>, 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.</jats:p>
Heath-Apostolopoulos I, Vargas-Ortiz D, Wilbraham L, et 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
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- Citations: 17
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
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- Citations: 27
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