Discovery of an Optimal Porous Crystalline Material for the Capture of Chemical Warfare Agents

Chemical warfare agents (CWAs) are regarded as a critical challenge in our society. Here, we use a high-throughput computational screening strategy backed up by experimental validation to identify and synthesize a promising porous material for CWA removal under humid conditions. Starting with a database of 2,932 existing metal-organic framework (MOF) structures, we selected those possessing cavities big enough to adsorb well-known CWAs such as sarin, soman, and mustard gas as well as their nontoxic simulants. We used Widom method to reduce significantly the simulation time of water adsorption, allowing us to shortlist 156 hydrophobic MOFs where water will not compete with the CWAs to get adsorbed. We then moved to grand canonical Monte Carlo (GCMC) simulations to assess the removal capacity of CWAs. We selected the best candidates in terms of performance but also in terms of chemical stability and moved to synthesis and experimental breakthrough adsorption to probe the predicted, excellent performance. This computational-experimental work represents a fast and efficient approach to screen porous materials in applications that involve the presence of moisture

Data visualization for Industry 4.0 : a stepping-stone toward a digital future, bridging the gap between academia and industry

Here, we analyze the potential of advanced data-visualization dashboards as an enabling technology for Industry 4.0. High-dimensional, real-time visualization allows the graphical expression of complex process variables at a fraction of the cost of full-scale digitalization. It is therefore a more achievable goal for smaller firms looking to transition to digital manufacturing and poses a realistic stepping-stone approach for Industry 4.0

Wiz: a web-based tool for interactive visualization of big data

In an age of information, visualizing and discerning meaning from data is as important as its collection. Interactive data visualization addresses both fronts by allowing researchers to explore data beyond what static images can offer. Here, we present Wiz, a web-based application for handling and visualizing large amounts of data. Wiz does not require programming or downloadable software for its use and allows scientists and non-scientists to unravel the complexity of data by splitting their relationships through 5D visual analytics, performing multivariate data analysis, such as principal component and linear discriminant analyses, all in vivid, publication-ready figures. With the explosion of high-throughput practices for materials discovery, information streaming capabilities, and the emphasis on industrial digitalization and artificial intelligence, we expect Wiz to serve as an invaluable tool to have a broad impact in our world of big data

Augmented reality for enhanced visualization of MOF adsorbents

Augmented reality (AR) is an emerging technique used to improve visualization and comprehension of complex 3D materials. This approach has been applied not only in the field of chemistry but also in real estate, physics, mechanical engineering, and many other areas. Here, we demonstrate the workflow for an app-free AR technique for visualization of metal-organic frameworks (MOFs) and other porous materials to investigate their crystal structures, topology, and gas adsorption sites. We think this workflow will serve as an additional tool for computational and experimental scientists working in the field for both research and educational purposes

Targeted classification of metal–organic frameworks in the Cambridge structural database (CSD)

Large-scale targeted exploration of metal–organic frameworks (MOFs) with characteristics such as specific surface chemistry or metal-cluster family has not been investigated so far. These definitions are particularly important because they can define the way MOFs interact with specific molecules (e.g. their hydrophilic/phobic character) or their physicochemical stability. We report here the development of algorithms to break down the overarching family of MOFs into a number of subgroups according to some of their key chemical and physical features. Available within the Cambridge Crystallographic Data Centre's (CCDC) software, we introduce new approaches to allow researchers to browse and efficiently look for targeted MOF families based on some of the most well-known secondary building units. We then classify them in terms of their crystalline properties: metal-cluster, network and pore dimensionality, surface chemistry (i.e. functional groups) and chirality. This dynamic database and family of algorithms allow experimentalists and computational users to benefit from the developed criteria to look for specific classes of MOFs but also enable users – and encourage them – to develop additional MOF queries based on desired chemistries. These tools are backed-up by an interactive web-based data explorer containing all the data obtained. We also demonstrate the usefulness of these tools with a high-throughput screening for hydrogen storage at room temperature. This toolbox, integrated in the CCDC software, will guide future exploration of MOFs and similar materials, as well as their design and development for an ever-increasing range of potential applications

Computational techniques for characterisation of electrically conductive MOFs : quantum calculations and machine learning approaches

The customisability of metal–organic frameworks (MOFs) has attracted exponentially growing interest in the realm of materials science. Because of their porous nature, MOF research has been primarily focused on gas storage and separation. More recent investigations into MOFs have realised promising electronic characteristics suitable for applications in electrocatalysis, resistive sensing and energy storage. Despite high porosity and presence of organic linkers, — properties that contribute to the electrical insulating properties of most MOFs — several strategies have been developed to construct MOFs with high conductivity. These recent findings serve as strong encouragement that the incorporation of charge transport chemistries into MOFs leads to structures that exhibit conductive behaviour. However, our understanding behind the nature of conductivity in MOFs is not yet explicitly evident. The development of outstanding conductive MOFs would be greatly accelerated if we had an atomistic-level understanding of how low-energy charge transport pathways can be installed in MOFs. In this context, computational quantum mechanical methods can produce rich electronic structure details with sufficient accuracy to provide insights towards MOFs’ conductive behaviour. An emerging alternative design strategy is the use of machine learning to accelerate the way we screen and discover new conductive materials. In this review, we summarise the most widely used quantum mechanical techniques to characterise important band structure parameters and compare them with experimental measurements in the MOF literature. We also highlight the current state of the art in machine learning assisted screening of MOFs for their conductive properties and discuss the opportunities and challenges which lie ahead in this exciting field

DigiMOF: a database of metal–organic framework synthesis information generated via text mining

The vastness of materials space, particularly that which is concerned with metal-organic frameworks (MOFs), creates the critical problem of performing efficient identification of promising materials for specific applications. Although high-throughput computational approaches, including the use of machine learning, have been useful in rapid screening and rational design of MOFs, they tend to neglect descriptors related to their synthesis. One way to improve the efficiency of MOF discovery is to data-mine published MOF papers to extract the materials informatics knowledge contained within journal articles. Here, by adapting the chemistry-aware natural language processing tool, ChemDataExtractor (CDE), we generated an open-source database of MOFs focused on their synthetic properties: the DigiMOF database. Using the CDE web scraping package alongside the Cambridge Structural Database (CSD) MOF subset, we automatically downloaded 43,281 unique MOF journal articles, extracted 15,501 unique MOF materials, and text-mined over 52,680 associated properties including the synthesis method, solvent, organic linker, metal precursor, and topology. Additionally, we developed an alternative data extraction technique to obtain and transform the chemical names assigned to each CSD entry in order to determine linker types for each structure in the CSD MOF subset. This data enabled us to match MOFs to a list of known linkers provided by Tokyo Chemical Industry UK Ltd. (TCI) and analyze the cost of these important chemicals. This centralized, structured database reveals the MOF synthetic data embedded within thousands of MOF publications and contains further topology, metal type, accessible surface area, largest cavity diameter, pore limiting diameter, open metal sites, and density calculations for all 3D MOFs in the CSD MOF subset. The DigiMOF database and associated software are publicly available for other researchers to rapidly search for MOFs with specific properties, conduct further analysis of alternative MOF production pathways, and create additional parsers to search for additional desirable properties

Gas adsorption and framework flexibility of CALF-20 explored via experiments and simulations

In 2021, Svante, in collaboration with BASF, reported successful scale up of CALF-20 production, a stable MOF with high capacity for post-combustion CO2 capture which exhibits remarkable stability towards water. CALF-20’s success story in the MOF commercialisation space provides new thinking about appropriate structural and adsorptive metrics important for CO2 capture. Here, we combine atomistic-level simulations with experiments to study adsorptive properties of CALF-20 and shed light on its flexible crystal structure. We compare measured and predicted CO2 and water adsorption isotherms and explain the role of water-framework interactions and hydrogen bonding networks in CALF-20’s hydrophobic behaviour. Furthermore, regular and enhanced sampling molecular dynamics simulations are performed with both density-functional theory (DFT) and machine learning potentials (MLPs) trained to DFT energies and forces. From these simulations, the effects of adsorption-induced flexibility in CALF-20 are uncovered. We envisage this work would encourage development of other MOF materials useful for CO2 capture applications in humid conditions

Electrochemically addressable trisradical rotaxanes organized within a metal–organic framework

The organization of trisradical rotaxanes within the channels of a Zr6-based metal–organic framework (NU-1000) has been achieved postsynthetically by solvent-assisted ligand incorporation. Robust ZrIV–carboxylate bonds are forged between the Zr clusters of NU-1000 and carboxylic acid groups of rotaxane precursors (semirotaxanes) as part of this building block replacement strategy. Ultraviolet–visible–near-infrared (UV-Vis-NIR), electron paramagnetic resonance (EPR), and 1H nuclear magnetic resonance (NMR) spectroscopies all confirm the capture of redox-active rotaxanes within the mesoscale hexagonal channels of NU-1000. Cyclic voltammetry measurements performed on electroactive thin films of the resulting material indicate that redox-active viologen subunits located on the rotaxane components can be accessed electrochemically in the solid state. In contradistinction to previous methods, this strategy for the incorporation of mechanically interlocked molecules within porous materials circumvents the need for de novo synthesis of a metal–organic framework, making it a particularly convenient approach for the design and creation of solid-state molecular switches and machines. The results presented here provide proof-of-concept for the application of postsynthetic transformations in the integration of dynamic molecular machines with robust porous frameworks