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From kitchen sieves and strainers to coffee filters, porous materials have a wide range of uses. On an industrial scale, they are used as sorbents, filters, membranes, and catalysts. Slater and Cooper review how each application will limit the materials that can be used, and also the size and connectivity of the pores required. They go on to compare and contrast a growing range of porous materials that are finding increasing use in academic and industrial applications. Science , this issue 10.1126/science.aaa8075 Porous solids are important as membranes, adsorbents, catalysts, and in other chemical applications. But for these materials to find greater use at an industrial scale, it is necessary to optimize multiple functions in addition to pore structure and surface area, such as stability, sorption kinetics, processability, mechanical properties, and thermal properties. Several different classes of porous solids exist, and there is no one-size-fits-all solution; it can therefore be challenging to choose the right type of porous material for a given job. Computational prediction of structure and properties has growing potential to complement experiment to identify the best porous materials for specific applications.

Porosity is a rare property for molecular materials but, surprisingly, porous solids built from discrete organic cage molecules have emerged as a versatile functional-materials platform. From modest beginnings less than a decade ago, there are now organic cage solids with surface areas that can rival extended metal–organic frameworks. In contrast to network polymers and frameworks, these organic cages are synthesized first and then assembled in the solid state in a separate step. This offers solution-processing options that are not available for insoluble organic and inorganic frameworks. In this Review, we highlight examples of porous organic cages and focus on the unique features that set them apart, such as their molecular solubility, their increased tendency to exhibit polymorphism and the scope for modular co-crystallization. The surface areas of molecular organic cage solids now rival those of metal–organic frameworks. In this Review, the synthesis and structures of various porous organic cages are outlined together with a discussion of the characteristics — such as solubility, polymorphism and modular co-crystallization — that distinguish these cages from their inorganic or hybrid counterparts.

Metal–organic frameworks (MOFs) are useful synthetic materials that are built by the programmed assembly of metal nodes and organic linkers 1 . The success of MOFs results from the isoreticular principle 2 , which allows families of structurally analogous frameworks to be built in a predictable way. This relies on directional coordinate covalent bonding to define the framework geometry. However, isoreticular strategies do not translate to other common crystalline solids, such as organic salts 3 – 5 , in which the intermolecular ionic bonding is less directional. Here we show that chemical knowledge can be combined with computational crystal-structure prediction 6 (CSP) to design porous organic ammonium halide salts that contain no metals. The nodes in these salt frameworks are tightly packed ionic clusters that direct the materials to crystallize in specific ways, as demonstrated by the presence of well-defined spikes of low-energy, low-density isoreticular structures on the predicted lattice energy landscapes 7 , 8 . These energy landscapes allow us to select combinations of cations and anions that will form thermodynamically stable, porous salt frameworks with channel sizes, functionalities and geometries that can be predicted a priori. Some of these porous salts adsorb molecular guests such as iodine in quantities that exceed those of most MOFs, and this could be useful for applications such as radio-iodine capture 9 – 12 . More generally, the synthesis of these salts is scalable, involving simple acid–base neutralization, and the strategy makes it possible to create a family of non-metal organic frameworks that combine high ionic charge density with permanent porosity. The use of computational crystal-structure prediction has enabled the targeted assembly of frameworks of porous organic ammonium halide salts that have many of the qualities of metal–organic frameworks despite containing no metal.

Covalent organic frameworks (COFs) are distinguished from other organic polymers by their crystallinity 1 – 3 , but it remains challenging to obtain robust, highly crystalline COFs because the framework-forming reactions are poorly reversible 4 , 5 . More reversible chemistry can improve crystallinity 6 – 9 , but this typically yields COFs with poor physicochemical stability and limited application scope 5 . Here we report a general and scalable protocol to prepare robust, highly crystalline imine COFs, based on an unexpected framework reconstruction. In contrast to standard approaches in which monomers are initially randomly aligned, our method involves the pre-organization of monomers using a reversible and removable covalent tether, followed by confined polymerization. This reconstruction route produces reconstructed COFs with greatly enhanced crystallinity and much higher porosity by means of a simple vacuum-free synthetic procedure. The increased crystallinity in the reconstructed COFs improves charge carrier transport, leading to sacrificial photocatalytic hydrogen evolution rates of up to 27.98 mmol h −1  g −1 . This nanoconfinement-assisted reconstruction strategy is a step towards programming function in organic materials through atomistic structural control. A protocol in which monomers are pre-organized using a reversible and removable urea linkage enables the production of covalent organic frameworks with higher crystallinity and porosity than those produced using standard approaches with randomly aligned monomers.

Nature uses organic molecules for light harvesting and photosynthesis, but most man-made water splitting catalysts are inorganic semiconductors. Organic photocatalysts, while attractive because of their synthetic tunability, tend to have low quantum efficiencies for water splitting. Here we present a crystalline covalent organic framework (COF) based on a benzo-bis(benzothiophene sulfone) moiety that shows a much higher activity for photochemical hydrogen evolution than its amorphous or semicrystalline counterparts. The COF is stable under long-term visible irradiation and shows steady photochemical hydrogen evolution with a sacrificial electron donor for at least 50 hours. We attribute the high quantum efficiency of fused-sulfone-COF to its crystallinity, its strong visible light absorption, and its wettable, hydrophilic 3.2 nm mesopores. These pores allow the framework to be dye-sensitized, leading to a further 61% enhancement in the hydrogen evolution rate up to 16.3 mmol g−1 h−1. The COF also retained its photocatalytic activity when cast as a thin film onto a support.

Nanosizing confers unique functions in materials such as graphene and quantum dots. Here, we present two nanoscale-covalent organic frameworks (nano-COFs) that exhibit exceptionally high activity for photocatalytic hydrogen production that results from their size and morphology. Compared to bulk analogues, the downsizing of COFs crystals using surfactants provides greatly improved water dispersibility and light-harvesting properties. One of these nano-COFs shows a hydrogen evolution rate of 392.0 mmol g −1 h −1 (33.3 μmol h −1 ), which is one of the highest mass-normalized rates reported for a COF or any other organic photocatalysts. A reverse concentration-dependent photocatalytic phenomenon is observed, whereby a higher photocatalytic activity is found at a lower catalyst concentration. These materials also show a molecule-like excitonic nature, as studied by photoluminescence and transient absorption spectroscopy, which is again a function of their nanoscale dimensions. This charts a new path to highly efficient organic photocatalysts for solar fuel production. Nanosizing covalent organic frameworks using surfactants provides greatly improved water dispersibility and light-harvesting properties, leading to dramatically enhanced photocatalytic hydrogen production performance. Here the authors observe a reverse concentration-dependent photocatalytic phenomeno, whereby a higher photocatalytic activity is found at a lower catalyst concentration.

The separation of hydrogen isotopes for applications such as nuclear fusion is a major challenge. Current technologies are energy intensive and inefficient. Nanoporous materials have the potential to separate hydrogen isotopes by kinetic quantum sieving, but high separation selectivity tends to correlate with low adsorption capacity, which can prohibit process scale-up. In this study, we use organic synthesis to modify the internal cavities of cage molecules to produce hybrid materials that are excellent quantum sieves. By combining small-pore and large-pore cages together in a single solid, we produce a material with optimal separation performance that combines an excellent deuterium/hydrogen selectivity (8.0) with a high deuterium uptake (4.7 millimoles per gram).

Molecular crystals cannot be designed in the same manner as macroscopic objects, because they do not assemble according to simple, intuitive rules. Their structures result from the balance of many weak interactions, rather than from the strong and predictable bonding patterns found in metal–organic frameworks and covalent organic frameworks. Hence, design strategies that assume a topology or other structural blueprint will often fail. Here we combine computational crystal structure prediction and property prediction to build energy–structure–function maps that describe the possible structures and properties that are available to a candidate molecule. Using these maps, we identify a highly porous solid, which has the lowest density reported for a molecular crystal so far. Both the structure of the crystal and its physical properties, such as methane storage capacity and guest-molecule selectivity, are predicted using the molecular structure as the only input. More generally, energy–structure–function maps could be used to guide the experimental discovery of materials with any target function that can be calculated from predicted crystal structures, such as electronic structure or mechanical properties. Energy–structure–function maps that describe the possible structures and properties of molecular crystals are developed, and these maps are used to guide the experimental discovery of porous materials with specific functions. Energy–structure–function maps of crystals It can be difficult to predict and optimize the structure and properties of crystalline molecular compounds without resorting to a 'wet' synthesis. Rather than crystallizing according to simple rules, their structures result from the combination of many weak interactions. Computational calculations and screening can help, but are limited by the computational power available and often rely on prior knowledge and assumptions about the structure. Here, Graeme Day and colleagues use structure prediction maps to computationally scan various packings for small molecular crystals, and assign functions, such as porosity or gas storage capacity, to each plausible structure found. These energy–structure–function maps can guide synthetic work by ruling out seemingly promising structures that are calculated to have undesirable properties. To validate the method, the authors synthesize a molecule that crystallizes as a porous solid with a record low density for a porous organic compound.

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. Ion-selective membranes are widely used for water purification and electrochemical energy devices but designing their pore architectures is challenging. Membranes with narrow channels and hydrophilic functionality are shown to exhibit salt ions transport and selectivity towards small organic molecules.

Conjugated organic photoredox catalysts (OPCs) can promote a wide range of chemical transformations. It is challenging to predict the catalytic activities of OPCs from first principles, either by expert knowledge or by using a priori calculations, as catalyst activity depends on a complex range of interrelated properties. Organic photocatalysts and other catalyst systems have often been discovered by a mixture of design and trial and error. Here we report a two-step data-driven approach to the targeted synthesis of OPCs and the subsequent reaction optimization for metallophotocatalysis, demonstrated for decarboxylative sp 3 – sp 2 cross-coupling of amino acids with aryl halides. Our approach uses a Bayesian optimization strategy coupled with encoding of key physical properties using molecular descriptors to identify promising OPCs from a virtual library of 560 candidate molecules. This led to OPC formulations that are competitive with iridium catalysts by exploring just 2.4% of the available catalyst formulation space (107 of 4,500 possible reaction conditions). Organic photoredox catalysts enable diverse chemical transformations, but predicting their activity is challenging due to complex properties. Now, a two-step data-driven approach is introduced for targeted organic photoredox catalysts synthesis and reaction optimization. Using Bayesian optimization, promising catalysts can be efficiently identified, yielding competitive results with iridium catalysts.