Topological Characterization of Metal-Organic Frameworks: A Perspective.

Metal-organic frameworks (MOFs) began to emerge over two decades ago, resulting in the deposition of 120 000 MOF-like structures (and counting) into the Cambridge Structural Database (CSD). Topological analysis is a critical step toward understanding periodic MOF materials, offering insight into the design and synthesis of these crystals via the simplification of connectivity imposed on the complete chemical structure. While some of the most prevalent topologies, such as face-centered cubic (), square lattice (), and diamond (), are simple and can be easily assigned to structures, MOFs that are built from complex building blocks, with multiple nodes of different symmetry, result in difficult to characterize topological configurations.

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 CO 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 CO capture. Here, we combine atomistic-level simulations with experiments to study adsorptive properties of CALF-20 and shed light on its flexible crystal structure.

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.

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.

Computational Characterization of Zr-Oxide MOFs for Adsorption Applications.

Zr-oxide secondary building units construct metal-organic framework (MOF) materials with excellent gas adsorption properties and high mechanical, thermal, and chemical stability. These attributes have led Zr-oxide MOFs to be well-recognized for a wide range of applications, including gas storage and separation, catalysis, as well as healthcare domain. Here, we report structure search methods within the Cambridge Structural Database (CSD) to create a curated subset of 102 Zr-oxide MOFs synthesized to date, bringing a unique record for all researchers working in this area.

Hydrogen storage in MOFs: Machine learning for finding a needle in a haystack.

In recent years, machine learning (ML) has grown exponentially within the field of structure property predictions in materials science. In this issue of , Ahmed and Siegel scrutinize several redeveloped ML techniques for systematic investigations of over 900,000 metal-organic framework (MOF) structures, taken from 19 databases, to discover new, potentially record-breaking, hydrogen-storage materials.