Owing to the vast diversity of linkers, nodes, and topologies, metal–organic frameworks can be tailored for specific tasks, such as chemical separations or catalysis. Accordingly, these materials have attracted significant interest for capture and/or detoxification of toxic industrial chemicals and chemical warfare agents. In this paper, we review recent experimental and computational work pertaining to the capture of several industrially-relevant toxic chemicals, including NH₃, SO₂, NO₂, H₂S, and some volatile organic compounds, with particular emphasis on the challenging issue of designing materials that selectively adsorb these chemicals in the presence of water. We also examine recent research on the capture and catalytic degradation of chemical warfare agents such as sarin and sulfur mustard using metal–organic frameworks.

Delivering methane and hydrogen: The pressure for onboard storage of methane and hydrogen on vehicles is usually limited to 100 bar for the use of lightweight containers, but the amount stored can be increased with the use of absorbent materials. Efficient storage and delivery require a balance of volumetric and gravimetric storage. Chen et al. designed a metal-organic framework with trialuminum nodes and a large hexadentate aromatic linker that optimizes both parameters. This material surpassed the U.S. Department of Energy targets for methane and had a deliverable capacity of 14% by weight for hydrogen. Science , this issue p. 297

The low volumetric density of hydrogen is a major limitation to its use as a transportation fuel. Filling a fuel tank with nanoporous materials, such as metal–organic frameworks (MOFs), could greatly improve the deliverable capacity of these tanks if appropriate materials could be found. However, since MOFs can be made from many combinations of metal nodes, organic linkers, and functional groups, the design space of possible MOFs is enormous. Experimental characterization of thousands of MOFs is infeasible, and even conventional molecular simulations can be prohibitively expensive for large databases. In this work, we have developed a data-driven approach to accelerate materials screening and learn structure–property relationships. We report new descriptors for gas adsorption in MOFs derived from the energetics of MOF–guest interactions. Using the bins of an energy histogram as features, we trained a sparse regression model to predict gas uptake in multiple MOF databases to an accuracy within 3 g L⁻¹. The interpretable model parameters indicate that a somewhat weak attraction between hydrogen and the framework is ideal for cryogenic storage and release. Our machine learning method is more than three orders of magnitude faster than conventional molecular simulations, enabling rapid exploration of large numbers of MOFs. As a case study, we applied the method to screen a database of more than 50 000 experimental MOF structures. We experimentally validated one of the top candidates identified from the accelerated screening, MFU-4l. This material exhibited a hydrogen deliverable capacity of 47 g L⁻¹ (54 g L⁻¹ simulated) when operating at storage conditions of 77 K, 100 bar and delivery at 160 K, 5 bar.