Digital navigation of energy-structure-function maps for hydrogen-bonded porous molecular crystals

Ortho-Alkoxy-benzamide Directed Formation of a Single Crystalline Hydrogen-bonded Crosslinked Organic Framework and Its Boron Trifluoride Uptake and Catalysis

Boron trifluoride (BF3 ) is a highly corrosive gas widely used in industry. Confining BF3 in porous materials ensures safe and convenient handling and prevents its degradation. Hence, it is highly desired to develop porous materials with high adsorption capacity, high stability, and resistance to BF3 corrosion. Herein, we designed and synthesized a Lewis basic single-crystalline hydrogen-bond crosslinked organic framework (HC OF-50) for BF3 storage and its application in catalysis. Specifically, we introduced self-complementary ortho-alkoxy-benzamide hydrogen-bonding moieties to direct the formation of highly organized hydrogen-bonded networks, which were subsequently photo-crosslinked to generate HC OFs. The HC OF-50 features Lewis basic thioether linkages and electron-rich pore surfaces for BF3 uptake. As a result, HC OF-50 shows a record-high 14.2 mmol/g BF3 uptake capacity. The BF3 uptake in HC OF-50 is reversible, leading to the slow release of BF3 . We leveraged this property to reduce the undesirable chain transfer and termination in the cationic polymerization of vinyl ethers. Polymers with higher molecular weights and lower polydispersity were generated compared to those synthesized using BF3  ⋅ Et2 O. The elucidation of the structure-property relationship, as provided by the single-crystal X-ray structures, combined with the high BF3 uptake capacity and controlled sorption, highlights the molecular understanding of framework-guest interactions in addressing contemporary challenges.

Leveraging Machine Learning for Metal-Organic Frameworks: A Perspective

Metal-organic frameworks (MOFs) have attracted tremendous interest because of their tunable structures, functionalities, and physiochemical properties. The nearly infinite combinations of metal nodes and organic linkers have led to the synthesis of over 100,000 experimental MOFs and the construction of millions of hypothetical counterparts. It is intractable to identify the best candidates in the immense chemical space of MOFs for applications via conventional trial-to-error experiments or brute-force simulations. Over the past several years, machine learning (ML) has substantially transformed the way of MOF discovery, design, and synthesis. Driven by the abundant data from experiments or simulations, ML can not only efficiently and accurately predict MOF properties but also quantitatively derive structure-property relationships for rational design and screening. In this Perspective, we summarize recent achievements in leveraging ML for MOFs from the aspects of data acquisition, featurization, model training, and applications. Then, current challenges and new opportunities are discussed for the future exploration of ML to accelerate the development of new MOFs in this vibrant field.

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.

In situ characterisation for nanoscale structure-performance studies in electrocatalysis

Recently, electrocatalytic reactions involving oxygen, nitrogen, water, and carbon dioxide have been developed to substitute conventional chemical processes, with the aim of producing clean energy, fuels and chemicals. A deepened understanding of catalyst structures, active sites and reaction mechanisms plays a critical role in improving the performance of these reactions. To this end, in situ/operando characterisations can be used to visualise the dynamic evolution of nanoscale materials and reaction intermediates under electrolysis conditions, thus enhancing our understanding of heterogeneous electrocatalytic reactions. In this review, we summarise the state-of-the-art in situ characterisation techniques used in electrocatalysis. We categorise them into three sections based on different working principles: microscopy, spectroscopy, and other characterisation techniques. The capacities and limits of the in situ characterisation techniques are discussed in each section to highlight the present-day horizons and guide further advances in the field, primarily aiming at the users of these techniques. Finally, we look at challenges and possible strategies for further development of in situ techniques.

Multifunctional Porous Hydrogen-Bonded Organic Frameworks: Current Status and Future Perspectives

Hydrogen-bonded organic frameworks (HOFs), self-assembled from organic or metalated organic building blocks (also termed as tectons) by hydrogen bonding, π-π stacking, and other intermolecular interactions, have become an emerging class of multifunctional porous materials. So far, a library of HOFs with high porosity has been synthesized based on versatile tectons and supramolecular synthons. Benefiting from the flexibility and reversibility of H-bonds, HOFs feature high structural flexibility, mild synthetic reaction, excellent solution processability, facile healing, easy regeneration, and good recyclability. However, the flexible and reversible nature of H-bonds makes most HOFs suffer from poor structural designability and low framework stability. In this Outlook, we first describe the development and structural features of HOFs and summarize the design principles of HOFs and strategies to enhance their stability. Second, we highlight the state-of-the-art development of HOFs for diverse applications, including gas storage and separation, heterogeneous catalysis, biological applications, sensing, proton conduction, and other applications. Finally, current challenges and future perspectives are discussed.

Coarse-grained modelling to predict the packing of porous organic cages

How molecules pack has vital ramifications for their applications as functional molecular materials. Small changes in a molecule's functionality can lead to large, non-intuitive, changes in their global solid-state packing, resulting in difficulty in targeted design. Predicting the crystal structure of organic molecules from only their molecular structure is a well-known problem plaguing crystal engineering. Although relevant to the properties of many organic molecules, the packing behaviour of modular porous materials, such as porous organic cages (POCs), greatly impacts the properties of the material. We present a novel way of predicting the solid-state phase behaviour of POCs by using a simplistic model containing the dominant degrees of freedom driving crystalline phase formation. We employ coarse-grained simulations to systematically study how chemical functionality of pseudo-octahedral cages can be used to manipulate the solid-state phase formation of POCs. Our results support those of experimentally reported structures, showing that for cages which pack via their windows forming a porous network, only one phase is formed, whereas when cages pack via their windows and arenes, the phase behaviour is more complex. While presenting a lower computational cost route for predicting molecular crystal packing, coarse-grained models also allow for the development of design rules which we start to formulate through our results.

A data-driven and topological mapping approach for the a priori prediction of stable molecular crystalline hydrates

Predictions of the structures of stoichiometric, fractional, or nonstoichiometric hydrates of organic molecular crystals are immensely challenging due to the extensive search space of different water contents, host molecular placements throughout the crystal, and internal molecular conformations. However, the dry frameworks of these hydrates, especially for nonstoichiometric or isostructural dehydrates, can often be predicted from a standard anhydrous crystal structure prediction (CSP) protocol. Inspired by developments in the field of drug binding, we introduce an efficient data-driven and topologically aware approach for predicting organic molecular crystal hydrate structures through a mapping of water positions within the crystal structure. The method does not require a priori specification of water content and can, therefore, predict stoichiometric, fractional, and nonstoichiometric hydrate structures. This approach, which we term a mapping approach for crystal hydrates (MACH), establishes a set of rules for systematic determination of favorable positions for water insertion within predicted or experimental crystal structures based on considerations of the chemical features of local environments and void regions. The proposed approach is tested on hydrates of three pharmaceutically relevant compounds that exhibit diverse crystal packing motifs and void environments characteristic of hydrate structures. Overall, we show that our mapping approach introduces an advance in the efficient performance of hydrate CSP through generation of stable hydrate stoichiometries at low cost and should be considered an integral component for CSP workflows.

Mapping the Porous and Chemical Structure-Function Relationships of Trace CH3I Capture by Metal-Organic Frameworks using Machine Learning

Large-scale computational screening has become an indispensable tool for functional materials discovery. It, however, remains a challenge to adequately interrogate the large amount of data generated by a screening study. Here, we computationally screened 1087 metal-organic frameworks (MOFs), from the CoRE MOF 2014 database, for capturing trace amounts (300 ppmv) of methyl iodide (CH₃I); as a primary representative of organic iodides, CH₃¹²⁹I is one of the most difficult radioactive contaminants to separate. Furthermore, we demonstrate a simple and general approach for mapping and interrogating the high-dimensional structure-function data obtained by high-throughput screening; this involves learning two-dimensional embeddings of the high-dimensional data by applying unsupervised learning to encoded structural and chemical features of MOFs. The resulting various porous and chemical structure-function maps are human-interpretable, revealing not only top-performing MOFs but also complex structure-function correlations that are hidden when inspecting individual MOF features. These maps also alleviate the need of laborious visual inspection of a large number of MOFs by clustering similar MOFs, per the encoding features, into defined regions on the map. We also show that these structure-function maps are amenable to supervised classification of the performances of MOFs for trace CH₃I capture. We further show that the machine-learning models trained on the 1087 CoRE MOFs can be used to predict an unseen set of 250 MOFs randomly selected from a different MOF database, achieving high prediction accuracies.

Global analysis of the energy landscapes of molecular crystal structures by applying the threshold algorithm

Polymorphism in molecular crystals has important consequences for the control of materials properties and our understanding of crystallization. Computational methods, including crystal structure prediction, have provided important insight into polymorphism, but have usually been limited to assessing the relative energies of structures. We describe the implementation of the Monte Carlo threshold algorithm as a method to provide an estimate of the energy barriers separating crystal structures. By sampling the local energy minima accessible from multiple starting structures, the simulations yield a global picture of the crystal energy landscapes and provide valuable information on the depth of the energy minima associated with crystal structures. We present results from applying the threshold algorithm to four polymorphic organic molecular crystals, examine the influence of applying space group symmetry constraints during the simulations, and discuss the relationship between the structure of the energy landscape and the intermolecular interactions present in the crystals.

Machine-Learning-Guided Identification of Coordination Polymer Ligands for Crystallizing Separation of Cs/Sr

Separation of Cs/Sr is one of many coordination-chemistry-centered processes in the grand scheme of spent nuclear fuel reprocessing, a critical link for a sustainable nuclear energy industry. To deploy a crystallizing Cs/Sr separation technology, we planned to systematically screen and identify candidate ligands that can efficiently and selectively bind to Sr2+ and form coordination polymers. Therefore, we mined the Cambridge Structural Database for characteristic structural information and developed a machine-learning-guided methodology for ligand evaluation. The optimized machine-learning model, correlating the molecular structures of the ligands with the predicted coordinative properties, generated a ranking list of potential compounds for Cs/Sr selective crystallization. The Sr2+ sequestration capability and selectivity over Cs+ of the promising ligands identified (squaric acid and chloranilic acid) were subsequently confirmed experimentally, with commendable performances, corroborating the artificial-intelligence-guided strategy.

Permanent Porosity in Hydroxamate Titanium-Organic Polyhedra

Following the synthesis of hydroxamate titanium-organic frameworks, we now extend these siderophore-type linkers to the assembly of the first titanium-organic polyhedra displaying permanent porosity. Mixed-linker versions of this molecular cage (cMUV-11) are also used to demonstrate the effect of pore chemistry in accessing high surface areas of near 1200 m²·g^-1.

A Porous Chalcogen-Bonded Organic Framework

The manner of bonding between constituent atoms or molecules invariably influences the properties of materials. Perhaps no material family is more emblematic of this than porous frameworks, wherein the namesake modes of connectivity give rise to discrete subclasses with unique collections of properties. However, established framework classes often display offsetting advantages and disadvantages for a given application. Thus, there exists no universally applicable material, and the discovery of alternative modes of framework connectivity is highly desirable. Here we show that chalcogen bonding, a subclass of σ-hole bonding, is a viable mode of connectivity in low-density porous frameworks. Crystallization studies with the triptycene tris(1,2,5-selenadiazole) molecular tecton reveal how chalcogen bonding can template high-energy lattice structures and how solvent conditions can be rationalized to obtain molecularly programmed porous chalcogen-bonded organic frameworks (ChOFs). These results provide the first evidence that σ-hole bonding can be used to advance the diversity of porous framework materials.

Rapid Screening of Metal-Organic Frameworks for Propane/Propylene Separation by Synergizing Molecular Simulation and Machine Learning

At present, 100 000+ metal-organic frameworks (MOFs) have been synthesized, and it is challenging to identity the best candidate for a specific application. In this study, MOFs are rapidly screened via a hierarchical approach for propane/propylene (C₃H₈/C₃H₆) separation. First, the adsorption capacity and selectivity of C₃H₈/C₃H₆ mixture in "Computation-Ready, Experimental" (CoRE) MOFs are predicted via a molecular simulation (MS) method. The relationships between separation metrics and structural factors are established, and top-performing CoRE MOFs are identified. Then, machine learning (ML) models are trained and developed upon the CoRE MOFs using pore size, pore geometry, and framework chemistry as feature descriptors. By introducing binned pore size distributions and geometric descriptors, the accuracy of ML models is substantially improved. The feature importance of the descriptors is physically interpreted by the Gini impurities and Shapley Additive Explanations. Subsequently, the ML models are used to rapidly screen experimental "Cambridge Structural Database" (CSD) MOFs and hypothetical MOFs for C₃H₈/C₃H₆ separation. In the CSD MOFs, the out-of-sample predictions are found to agree well with simulation results, demonstrating the excellent transferability of the ML models from the CoRE to CSD MOFs. Moreover, nine CSD MOFs are identified to possess separation performance superior to top-performing CoRE MOFs. Finally, the similarity and diversity among experimental and hypothetical MOFs are visualized and compared by the t-Distributed Stochastic Neighbor Embedding (t-SNE) feature projections. Remarkably, the CoRE and CSD MOFs are revealed to share a close similarity in both chemical and geometric feature spaces. By synergizing MS and ML, the hierarchical approach developed in this study would advance the rapid screening of MOFs across different databases toward industrially important separation processes.

Visualization and Quantification of Geometric Diversity in Metal-Organic Frameworks

With ever-growing numbers of metal-organic framework (MOF) materials being reported, new computational approaches are required for a quantitative understanding of structure-property correlations in MOFs. Here, we show how structural coarse-graining and embedding ("unsupervised learning") schemes can together give new insights into the geometric diversity of MOF structures. Based on a curated data set of 1262 reported experimental structures, we automatically generate coarse-grained and rescaled representations which we couple to a kernel-based similarity metric and to widely used embedding schemes. This approach allows us to visualize the breadth of geometric diversity within individual topologies and to quantify the distributions of local and global similarities across the structural space of MOFs. The methodology is implemented in an openly available Python package and is expected to be useful in future high-throughput studies.

A Microporous Hydrogen-Bonded Organic Framework for the Efficient Capture and Purification of Propylene

Adsorptive separation of propylene/propane (C₃ H₆ /C₃ H₈ ) mixture is desired for its potential energy saving on replacing currently deployed and energy-intensive cryogenic distillation. Realizing efficient C₃ H₆ /C₃ H₈ separation in the emerging hydrogen-bonded organic frameworks (HOFs) is very challenging owing to the lack of functional sites for preferential gas binding. By virtue of crystal engineering, we herein report a functionalized HOF (HOF-16) with free -COOH sites for the efficient separation of C₃ H₆ /C₃ H₈ mixtures. Under ambient conditions, HOF-16 shows a significant C₃ H₆ /C₃ H₈ uptake difference (by 76 %) and selectivity (5.4) in contrast to other carboxylic acid-based HOFs. Modeling studies indicate that free -COOH groups together with the suitable pore confinement facilitate the recognition and high-density packing of gas molecules. The separation performance of HOF-16 was validated by breakthrough experiments. HOF-16 is stable towards strong acidity and water.

Digital navigation of energy-structure-function maps for hydrogen-bonded porous molecular crystals

Energy-structure-function (ESF) maps can aid the targeted discovery of porous molecular crystals by predicting the stable crystalline arrangements along with their functions of interest. Here, we compute ESF maps for a series of rigid molecules that comprise either a triptycene or a spiro-biphenyl core, functionalized with six different hydrogen-bonding moieties. We show that the positioning of the hydrogen-bonding sites, as well as their number, has a profound influence on the shape of the resulting ESF maps, revealing promising structure-function spaces for future experiments. We also demonstrate a simple and general approach to representing and inspecting the high-dimensional data of an ESF map, enabling an efficient navigation of the ESF data to identify 'landmark' structures that are energetically favourable or functionally interesting. This is a step toward the automated analysis of ESF maps, an important goal for closed-loop, autonomous searches for molecular crystals with useful functions.