Predicting solvent effects on the structure of porous organic molecules

Streamlining the automated discovery of porous organic cages

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

Size Determination of Organic Cages by Diffusion NMR Spectroscopy

Reliable structure elucidation of covalent organic cage compounds remains challenging as routine analysis might leave ambiguities. Diffusion-ordered NMR spectroscopy (DOSY) allows insight into the molecular size and mass of the species present in solution, but a systematic evaluation of the diffusion behavior for cage assemblies is rarely considered. Here we report the synthesis of four series of covalent organic cages based on tribenzotriquinacenes and diboronic acids with varying geometry and exohedral substituents. We provide a guideline for the consistent measurement of diffusion coefficients from 1 H-DOSY NMR spectroscopy, which was utilized to study the diffusion behavior for the whole set of cages and selected examples from the literature. For structurally similar cages, a linear correlation between the solvodynamic volume and the molecular mass allows precise size determination. For more complex systems, multiple parameters, such as window size or rigid exohedral functionalization. further modulate cage diffusion in solution.

Synthesis and VCD Spectroscopic Characterization of a Series of Azacryptands from a Chiral Valine-Based Derivative of Tris(2-aminoethyl)amine (TREN)

Utilizing experimental and computational vibrational circular dichroism (VCD) spectroscopy, we explored the conformational preferences of a series of chiral C3 -symmetric octaazacryptands with tris(2-aminoethyl)-amine head groups derived from valine. While the spectra of the smallest azacryptand with p-phenyl linkers and its elongated derivative with p-biphenyls linker were found to match well with the computed spectra, the computed conformational preferences of the m-biphenyl-based azacryptand did not seem to reflect the conformations dominating in chloroform solution. A detailed analysis revealed that structural changes resulting in a collapsed cage structure gave a notably better match with the experiment. It could subsequently be concluded from the VCD analysis, that the octaazacryptands prefer a collapsed structure, which is not predicted by density functional theory (DFT) calculations as the global minimum structures. These findings are expected to have consequences also for future studies on inclusion complexes of such azacryptands.

Computational modeling to assist in the discovery of supramolecular materials

Computational modeling is increasingly used to assist in the discovery of supramolecular materials. Supramolecular materials are typically primarily built from organic components that are self-assembled through noncovalent bonding and have potential applications, including in selective binding, sorption, molecular separations, catalysis, optoelectronics, sensing, and as molecular machines. In this review, the key areas where computational prediction can assist in the discovery of supramolecular materials, including in structure prediction, property prediction, and the prediction of how to synthesize a hypothetical material are discussed, before exploring the potential impact of artificial intelligence techniques on the field. Throughout, the importance of close integration with experimental materials discovery programs will be highlighted. A series of case studies from the author's work across some different supramolecular material classes will be discussed, before finishing with a discussion of the outlook for the field.

Using Machine Learning and Data Mining to Leverage Community Knowledge for the Engineering of Stable Metal-Organic Frameworks

Although the tailored metal active sites and porous architectures of MOFs hold great promise for engineering challenges ranging from gas separations to catalysis, a lack of understanding of how to improve their stability limits their use in practice. To overcome this limitation, we extract thousands of published reports of the key aspects of MOF stability necessary for their practical application: the ability to withstand high temperatures without degrading and the capacity to be activated by removal of solvent molecules. From nearly 4000 manuscripts, we use natural language processing and image analysis to obtain over 2000 solvent-removal stability measures and 3000 thermal degradation temperatures. We analyze the relationships between stability properties and the chemical and geometric structures in this set to identify limits of prior heuristics derived from smaller sets of MOFs. By training predictive machine learning (ML, i.e., Gaussian process and artificial neural network) models to encode the structure-property relationships with graph- and pore-structure-based representations, we are able to make predictions of stability orders of magnitude faster than conventional physics-based modeling or experiment. Interpretation of important features in ML models provides insights that we use to identify strategies to engineer increased stability into typically unstable 3d-transition-metal-containing MOFs that are frequently targeted for catalytic applications. We expect our approach to accelerate the time to discovery of stable, practical MOF materials for a wide range of applications.

Chiral Self-sorting of Giant Cubic [8+12] Salicylimine Cage Compounds

Chiral self-sorting is intricately connected to the complicated chiral processes observed in nature and no artificial systems of comparably complexity have been generated by chemists. However, only a few examples of purely organic molecules have been reported so far, where the self-sorting process could be controlled. Herein, we describe the chiral self-sorting of large cubic [8+12] salicylimine cage compounds based on a chiral TBTQ precursor. Out of 23 possible cage isomers only the enantiopure and a meso cage were observed to be formed, which have been unambiguously characterized by single crystal X-ray diffraction. Furthermore, by careful choice of solvent the formation of meso cage could be controlled. With internal diameters of din =3.3-3.5 nm these cages are among the largest organic cage compounds characterized and show very high specific surface areas up to approx. 1500 m2  g-1 after desolvation.

Inducing Social Self-Sorting in Organic Cages To Tune The Shape of The Internal Cavity

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.

High-Throughput Approaches for the Discovery of Supramolecular Organic Cages

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.

Solvent-controlled self-assembly of tetrapodal [4 + 4] phosphate organic molecular cage

Two flexible subcomponents, namely tris(4-formylphenyl)phosphate and tris(2-aminoethyl)amine, are assembled into a tetrapodal [4 + 4] cage depending on the solvent effect. Single-crystal structure analysis reveals that the caivity is surrounded by four phosphate uints. Good selectivity of CO₂ adsorption over CH₄ is demonstrated by the gas adsorption experiment.

Computational discovery of molecular C60 encapsulants with an evolutionary algorithm

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.

stk: A python toolkit for supramolecular assembly

A tool for the automated assembly, molecular optimization and property calculation of supramolecular materials is presented. stk is a modular, extensible and open-source Python library that provides a simple Python API and integration with third party computational codes. stk currently supports the construction of linear polymers, small linear 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 explore the potential energy landscape of the assembled supramolecule and then calculate the supramolecule's structural features and properties. stk provides support for high-throughput screening of large batches of supramolecules at a time. The source code of the program can be found at https://github.com/supramolecular-toolkit/stk. © 2018 Wiley Periodicals, Inc.

High-throughput discovery of organic cages and catenanes using computational screening fused with robotic synthesis

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.

Porous Molecular Solids and Liquids

Until recently, porous molecular solids were isolated curiosities with properties that were eclipsed by porous frameworks, such as metal-organic frameworks. Now molecules have emerged as a functional materials platform that can have high levels of porosity, good chemical stability, and, uniquely, solution processability. The lack of intermolecular bonding in these materials has also led to new, counterintuitive states of matter, such as porous liquids. Our ability to design these materials has improved significantly due to advances in computational prediction methods.

Topological landscapes of porous organic cages

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.

Application of computational methods to the design and characterisation of porous molecular materials

Composed from discrete units, porous molecular materials (PMMs) possess properties not observed for conventional, extended solids. Molecular simulations provide crucial understanding for the design and characterisation of these unique materials.

Control of Uniform and Interconnected Macroporous Structure in PolyHIPE for Enhanced CO2 Adsorption/Desorption Kinetics

The highly uniform and interconnected macroporous polymer materials were prepared within the high internal phase hydrosol-in-oil emulsions (HIPEs). Impregnated with polyethylenimine (PEI), the polyHIPEs were then employed as solid adsorbents for CO2 capture. Thermodynamic and kinetic capture-and-release tests were performed with pure CO2, 10% CO2/N2, and moist CO2, respectively. It has shown that the polyHIPE with suitable surface area and PEI impregnation exhibits high CO2 adsorption capacity, remarkable CO2/N2 selectivity, excellent adsorption/desorption kinetics, enhanced efficiency in the presence of water, and admirable stability in capture and release cycles. The results demonstrate the superior comprehensive performance of the present PEI-impregnated polyHIPE for CO2 capture from the postcombustion flue gas.