Energy-Structure-Function Maps: Cartography for Materials Discovery

Design and assembly of porous organic cages

Porous organic cages (POCs) represent a notable category of porous materials, showing remarkable material properties due to their inherent porosity. Unlike extended frameworks which are constructed by strong covalent or coordination bonds, POCs are composed of discrete molecular units held together by weak intermolecular forces. Their structure and chemical traits can be systematically tailored, making them suitable for a range of applications including gas storage and separation, molecular separation and recognition, catalysis, and proton and ion conduction. This review provides a comprehensive overview of POCs, covering their synthesis methods, structure and properties, computational approaches, and applications, serving as a primer for those who are new to the domain. A special emphasis is placed on the growing role of computational methods, highlighting how advanced data-driven techniques and automation are increasingly aiding the rapid exploration and understanding of POCs. We conclude by addressing the prevailing challenges and future prospects in the field.

Frontiers of molecular crystal structure prediction for pharmaceuticals and functional organic materials

The reliability of organic molecular crystal structure prediction has improved tremendously in recent years. Crystal structure predictions for small, mostly rigid molecules are quickly becoming routine. Structure predictions for larger, highly flexible molecules are more challenging, but their crystal structures can also now be predicted with increasing rates of success. These advances are ushering in a new era where crystal structure prediction drives the experimental discovery of new solid forms. After briefly discussing the computational methods that enable successful crystal structure prediction, this perspective presents case studies from the literature that demonstrate how state-of-the-art crystal structure prediction can transform how scientists approach problems involving the organic solid state. Applications to pharmaceuticals, porous organic materials, photomechanical crystals, organic semi-conductors, and nuclear magnetic resonance crystallography are included. Finally, efforts to improve our understanding of which predicted crystal structures can actually be produced experimentally and other outstanding challenges are discussed.

Pore Modulation of Hydrogen-Bonded Organic Frameworks for Efficient Separation of Propylene

Developing hydrogen-bonded organic frameworks (HOFs) that combine functional sites, size control, and storage capability for targeting gas molecule capture is a novel and challenging venture. However, there is a lack of effective strategies to tune the hydrogen-bonded network to achieve high-performance HOFs. Here, a series of HOFs termed as HOF-ZSTU-M (M=1, 2, and 3) with different pore structures are obtained by introducing structure-directing agents (SDAs) into the hydrogen-bonding network of tetrakis (4-carboxyphenyl) porphyrin (TCPP). These HOFs have distinct space configurations with pore channels ranging from discrete to continuous multi-dimensional. Single-crystal X-ray diffraction (SCXRD) analysis reveals a rare diversity of hydrogen-bonding models dominated by SDAs. HOF-ZSTU-2, which forms a strong layered hydrogen-bonding network with ammonium (NH4 + ) through multiple carboxyl groups, has a suitable 1D "pearl-chain" channel for the selective capture of propylene (C3 H6 ). At 298 K and 1 bar, the C3 H6 storage density of HOF-ZSTU-2 reaches 0.6 kg L-1 , representing one of the best C3 H6 storage materials, while offering a propylene/propane (C3 H6 /C3 H8 ) selectivity of 12.2. Theoretical calculations and in situ SCXRD provide a detailed analysis of the binding strength of C3 H6 at different locations in the pearl-chain channel. Dynamic breakthrough tests confirm that HOF-ZSTU-2 can effectively separate C3 H6 from multi-mixtures.

Industrial Separation Challenges: How Does Supramolecular Chemistry Help?

The chemical industry and the chemical processes underscoring it are under intense scrutiny as the demands for the transition to more sustainable and environmentally friendly practices are increasing. Traditional industrial separation systems, such as thermally driven distillation for hydrocarbon purification, are energy intensive. The development of more energy efficient separation technologies is thus emerging as a critical need, as is the creation of new materials that may permit a transition away from classic distillation-based separations. In this Perspective, we focus on porous organic cages and macrocycles that can adsorb guest molecules selectively through various host-guest interactions and permit molecular sieving behavior at the molecular level. Specifically, we summarize the recent advances where receptor-based adsorbent materials have been shown to be effective for industrially relevant hydrocarbon separations, highlighting the underlying host-guest interactions that impart selectivity and permit the observed separations. This approach to sustainable separations is currently in its infancy. Nevertheless, several receptor-based adsorbent materials with extrinsic/intrinsic voids or special functional groups have been reported in recent years that can selectively capture various targeted guest molecules. We believe that the understanding of the interactions that drive selectivity at a molecular level accruing from these initial systems will permit an ever-more-effective "bottom-up" design of tailored molecular sieves that, in due course, will allow adsorbent material-based approaches to separations to transition from the laboratory into an industrial setting.

Experimental Confirmation of a Predicted Porous Hydrogen-Bonded Organic Framework

Hydrogen-bonded organic frameworks (HOFs) with low densities and high porosities are rare and challenging to design because most molecules have a strong energetic preference for close packing. Crystal structure prediction (CSP) can rank the crystal packings available to an organic molecule based on their relative lattice energies. This has become a powerful tool for the a priori design of porous molecular crystals. Previously, we combined CSP with structure-property predictions to generate energy-structure-function (ESF) maps for a series of triptycene-based molecules with quinoxaline groups. From these ESF maps, triptycene trisquinoxalinedione (TH5) was predicted to form a previously unknown low-energy HOF (TH5-A) with a remarkably low density of 0.374 g cm-3 and three-dimensional (3D) pores. Here, we demonstrate the reliability of those ESF maps by discovering this TH5-A polymorph experimentally. This material has a high accessible surface area of 3,284 m2  g-1 , as measured by nitrogen adsorption, making it one of the most porous HOFs reported to date.

Energy-structure-property relationships in uranium metal-organic frameworks

Located at the foot of the periodic table, uranium is a relatively underexplored element possessing rich chemistry. In addition to its high relevance to nuclear power, uranium shows promise for small molecule activation and photocatalysis, among many other powerful functions. Researchers have used metal-organic frameworks (MOFs) to harness uranium's properties, and in their quest to do so, have discovered remarkable structures and unique properties unobserved in traditional transition metal MOFs. More recently, (e.g. the last 8-10 years), theoretical calculations of framework energetics have supplemented structure-property studies in uranium MOFs (U-MOFs). In this Perspective, we summarize how these budding energy-structure-property relationships in U-MOFs enable a deeper understanding of chemical phenomena, enlarge chemical space, and elevate the field to targeted, rather than exploratory, discovery. Importantly, this Perspective encourages interdisciplinary connections between experimentalists and theorists by demonstrating how these collaborations have elevated the entire U-MOF field.

Into the Unknown: How Computation Can Help Explore Uncharted Material Space

Novel functional materials are urgently needed to help combat the major global challenges facing humanity, such as climate change and resource scarcity. Yet, the traditional experimental materials discovery process is slow and the material space at our disposal is too vast to effectively explore using intuition-guided experimentation alone. Most experimental materials discovery programs necessarily focus on exploring the local space of known materials, so we are not fully exploiting the enormous potential material space, where more novel materials with unique properties may exist. Computation, facilitated by improvements in open-source software and databases, as well as computer hardware has the potential to significantly accelerate the rational development of materials, but all too often is only used to postrationalize experimental observations. Thus, the true predictive power of computation, where theory leads experimentation, is not fully utilized. Here, we discuss the challenges to successful implementation of computation-driven materials discovery workflows, and then focus on the progress of the field, with a particular emphasis on the challenges to reaching novel materials.

Analogy Powered by Prediction and Structural Invariants: Computationally Led Discovery of a Mesoporous Hydrogen-Bonded Organic Cage Crystal

Mesoporous molecular crystals have potential applications in separation and catalysis, but they are rare and hard to design because many weak interactions compete during crystallization, and most molecules have an energetic preference for close packing. Here, we combine crystal structure prediction (CSP) with structural invariants to continuously qualify the similarity between predicted crystal structures for related molecules. This allows isomorphous substitution strategies, which can be unreliable for molecular crystals, to be augmented by a priori prediction, thus leveraging the power of both approaches. We used this combined approach to discover a rare example of a low-density (0.54 g cm^–3) mesoporous hydrogen-bonded framework (HOF), 3D-CageHOF-1. This structure comprises an organic cage (Cage-3-NH₂) that was predicted to form kinetically trapped, low-density polymorphs via CSP. Pointwise distance distribution structural invariants revealed five predicted forms of Cage-3-NH₂ that are analogous to experimentally realized porous crystals of a chemically different but geometrically similar molecule, T2. More broadly, this approach overcomes the difficulties in comparing predicted molecular crystals with varying lattice parameters, thus allowing for the systematic comparison of energy–structure landscapes for chemically dissimilar molecules.

HOFs Built from Hexatopic Carboxylic Acids: Structure, Porosity, Stability, and Photophysics

Hydrogen-bonded organic frameworks (HOFs) have attracted renewed attention as another type of promising candidates for functional porous materials. In most cases of HOF preparation, the applied molecular design principle is based on molecules with rigid π-conjugated skeleton together with more than three H-bonding groups to achieve 2D- or 3D-networked structures. However, the design principle does not always work, but results in formation of unexpected structures, where subtle structural factors of which we are not aware dictate the entire structure of HOFs. In this contribution, we assess recent advances in HOFs, focusing on those composed of hexatopic building block molecules, which can provide robust frameworks with a wide range of topologies and properties. The HOFs described in this work are classified into three types, depending on their H-bonded structural motifs. Here in, we focus on: (1) the chemical aspects that govern their unique fundamental chemistry and structures; and (2) their photophysics at the ensemble and single-crystal levels. The work addresses and discusses how these aspects affect and orient their photonic applicability. We trust that this contribution will provide a deep awareness and will help scientists to build up a systematic series of porous materials with the aim to control both their structural and photodynamical assets.

Using Molecular Design to Enhance the Coherence Time of Quintet Multiexcitons Generated by Singlet Fission in Single Crystals

Multiexciton quintet states, ⁵(TT), photogenerated in organic semiconductors using singlet fission (SF), consist of four quantum entangled spins, promising to enable new applications in quantum information science. However, the factors that determine the spin coherence of these states remain underexplored. Here, we engineer the packing of tetracene molecules within single crystals of 5,12-bis(tricyclohexylsilylethynyl)tetracene (TCHS-tetracene) to demonstrate a ⁵(TT) state that exhibits promising spin qubit properties, including a coherence time, T₂, = 3 μs at 10 K, a population lifetime, T_pop, = 130 μs at 5 K, and stability even at room temperature. The single-crystal platform also enables global alignment of the spins and, consequently, individual addressability of the spin-sublevel transitions. Decoherence mechanisms, including exciton diffusion, electronic dipolar coupling, and nuclear hyperfine interactions, are elucidated, providing design principles for increasing T₂ and the operational temperature of ⁵(TT). By dynamically decoupling ⁵(TT) from the surrounding spin bath, T₂ = 10 μs is achieved. These results demonstrate the viability of harnessing singlet fission to initiate multiple electron spins in a well-defined quantum state for next-generation molecular-based quantum technologies.

Progress in understanding crystallisation: a personal perspective

After this discussion meeting, most participants felt that we do not understand crystallisation. However, in the 1980s I believe that most scientists would have considered that crystallisation was adequately understood....

A Naturally Derived Nanocomposite Film with Photodynamic Antibacterial Activity: New Prospect for Sustainable Food Packaging

Food packaging with efficient antibacterial ability is highly desirable and challenging in facing the crisis of microbial contamination. However, most present packaging is based on metal-based antibacterial agents and requires a time-consuming antibacterial process. Here, the unique packaging (CC/BB films) featuring aggregation-induced emission behavior and photodynamic inactivation activity is prepared by dispersing self-assembled berberine-baicalin nanoparticles (BB NPs) into a mixed matrix of sodium carboxymethylcellulose-carrageenan (CC). The superiority of this design is that this packaging film can utilize sunlight to generate reactive oxygen species, thus eradicating more than 99% of E. coli and S. aureus within 60 min. Also, this film can release BB NPs to inactivate bacteria under all weather conditions. Surprisingly, the CC/BB nanocomposite film presented excellent mechanical performances (29.80 MPa and 38.65%), hydrophobicity (117.8°), and thermostability. The nanocomposite film is validated to be biocompatible and effective in protecting chicken samples, so this work will provide novel insights to explore safe and efficient antibacterial food packaging.

K-Ion Battery Cathode Design Utilizing Trigonal Prismatic Ligand Field

The intrinsic physical and chemical properties of materials are largely governed by the bonding and electronic structures of their fundamental building units. The majority of cathode materials contain octahedral TMO₆ (TM = transition metal), which dominates the redox chemistry during electrochemical operation. As a less symmetric form of TMO₆ , the trigonal prismatic geometry is not a traditionally favored coordination configuration as it tends to lose the crystal-field stabilization energy and thus generate large ligand repulsion. Herein, a K-ion battery cathode design, K₂ Fe(C₂ O₄ )₂ , is shown​, where the TMO₆ trigonal prism (TP) is not only electrochemically active but stable enough to allow for excellent cycling stability. Detailed synchrotron X-ray absorption spectroscopy measurements reveal the evolution of localized fine structure, evidencing the electrochemical activity, reversibility, and stability of the TP motif. The findings are expected to expand the toolbox for the rational design of electrode materials by taking advantage of TP as a structural gene.

Integrating Computational and Experimental Workflows for Accelerated Organic Materials Discovery

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 toward 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 realized. Herein, 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.

Structure prediction of crystals, surfaces and nanoparticles

We review the current techniques used in the prediction of crystal structures and their surfaces and of the structures of nanoparticles. The main classes of search algorithm and energy function are summarized, and we discuss the growing role of methods based on machine learning. We illustrate the current status of the field with examples taken from metallic, inorganic and organic systems. This article is part of a discussion meeting issue 'Dynamic in situ microscopy relating structure and function'.

Free-energy landscape of polymer-crystal polymorphism

Polymorphism rationalizes how processing can control the final structure of a material. The rugged free-energy landscape and exceedingly slow kinetics in the solid state have so far hampered computational investigations. We report for the first time the free-energy landscape of a polymorphic crystalline polymer, syndiotactic polystyrene. Coarse-grained metadynamics simulations allow us to efficiently sample the landscape at large. The free-energy difference between the two main polymorphs, α and β, is further investigated by quantum-chemical calculations. The results of the two methods are in line with experimental observations: they predict β as the more stable polymorph under standard conditions. Critically, the free-energy landscape suggests how the α polymorph may lead to experimentally observed kinetic traps. The combination of multiscale modeling, enhanced sampling, and quantum-chemical calculations offers an appealing strategy to uncover complex free-energy landscapes with polymorphic behavior.

Understanding the geometric diversity of inorganic and hybrid frameworks through structural coarse-graining

Much of our understanding of complex structures is based on simplification: for example, metal-organic frameworks are often discussed in the context of "nodes" and "linkers", allowing for a qualitative comparison with simpler inorganic structures. Here we show how such an understanding can be obtained in a systematic and quantitative framework, combining atom-density based similarity (kernel) functions and unsupervised machine learning with the long-standing idea of "coarse-graining" atomic structure. We demonstrate how the latter enables a comparison of vastly different chemical systems, and we use it to create a unified, two-dimensional structure map of experimentally known tetrahedral AB2 networks - including clathrate hydrates, zeolitic imidazolate frameworks (ZIFs), and diverse inorganic phases. The structural relationships that emerge can then be linked to microscopic properties of interest, which we exemplify for structural heterogeneity and tetrahedral density.

Evolutionary chemical space exploration for functional materials: computational organic semiconductor discovery

Computational methods, including crystal structure and property prediction, have the potential to accelerate the materials discovery process by enabling structure prediction and screening of possible molecular building blocks prior to their synthesis. However, the discovery of new functional molecular materials is still limited by the need to identify promising molecules from a vast chemical space. We describe an evolutionary method which explores a user specified region of chemical space to identify promising molecules, which are subsequently evaluated using crystal structure prediction. We demonstrate the methods for the exploration of aza-substituted pentacenes with the aim of finding small molecule organic semiconductors with high charge carrier mobilities, where the space of possible substitution patterns is too large to exhaustively search using a high throughput approach. The method efficiently explores this large space, typically requiring calculations on only ∼1% of molecules during a search. The results reveal two promising structural motifs: aza-substituted naphtho[1,2-a]anthracenes with reorganisation energies as low as pentacene and a series of pyridazine-based molecules having both low reorganisation energies and high electron affinities.

Charge mobility calculation of organic semiconductors without use of experimental single-crystal data

Prediction of material properties of newly designed molecules is a long-term goal in organic electronics. In general, it is a difficult problem, because the material properties are dominated by the unknown packing structure. We present a practical method to obtain charge transport properties of organic single crystals, without use of experimental single-crystal data. As a demonstration, we employ the promising molecule C10-DNBDT. We succeeded in quantitative evaluation of charge mobility of the single crystal using our quantum wave-packet dynamical simulation method. Here, the single-crystal data is computationally obtained by searching possible packing structures from structural formula of the molecule. We increase accuracy in identifying the actual crystal structure from suggested ones by using not only crystal energy but also similarity between calculated and experimental powder X-ray diffraction patterns. The proposed methodology can be a theoretical design technique for efficiently developing new high-performance organic semiconductors, since it can estimate the charge transport properties at early stage in the process of material development.

Toward crystalline porosity estimators for porous molecules

Our data-mining of crystalline molecular materials reveals the correlations between the molecular and crystalline porosity.

Mining predicted crystal structure landscapes with high throughput crystallisation: old molecules, new insights

New crystal forms of two well-studied organic molecules are identified in a computationally targeted way, by combining structure prediction with a robotic crystallisation screen, including a ‘hidden’ porous polymorph of trimesic acid.

Organic molecules tend to close pack to form dense structures when they are crystallised from organic solvents. Porous molecular crystals defy this rule: they contain open space, which is typically stabilised by inclusion of solvent in the interconnected pores during crystallisation. The design and discovery of such structures is often challenging and time consuming, in part because it is difficult to predict solvent effects on crystal form stability. Here, we combine crystal structure prediction (CSP) with a robotic crystallisation screen to accelerate the discovery of stable hydrogen-bonded frameworks. We exemplify this strategy by finding new phases of two well-studied molecules in a computationally targeted way. Specifically, we find a new ‘hidden’ porous polymorph of trimesic acid, δ-TMA, that has a guest-free hexagonal pore structure, as well as three new solvent-stabilized diamondoid frameworks of adamantane-1,3,5,7-tetracarboxylic acid (ADTA). Beyond porous solids, this hybrid computational–experimental approach could be applied to a wide range of materials problems, such as organic electronics and drug formulation.

The influence of nitrogen position on charge carrier mobility in enantiopure aza[6]helicene crystals

A computational study exploring the influence of the nitrogen position on charge carrier mobility in enantiopure aza[6]helicene crystals

Switchable gate-opening effect in metal-organic polyhedra assemblies through solution processing

Gate-opening gas sorption is known for metal-organic frameworks, and is associated with structural flexibility and advantageous properties for sensing and gas uptake. Here, we show that gate-opening is also possible for metal-organic polyhedra (MOPs), and depends on the molecular organisation in the lattice. Thanks to the solubility of MOPs, several interchangeable solvatomorphs of a lantern-type MOP were synthesised via treatment with different solvents. One phase obtained through use of methanol induced a gate-opening effect in the lattice in response to carbon dioxide uptake. The sorption process was thoroughly investigated with in situ powder X-ray diffraction and simultaneous adsorption experiments. Meanwhile, solution processing of this flexible phase using THF led to a permanently porous phase without a gate-opening effect. Furthermore, we find that we can change the metallic composition of the MOP, and yet retain flexibility. By showing that gate-opening can be switched on and off depending on the solvent of crystallisation, these findings have implications for the solution-based processing of MOPs.