A novel computational methodology for large-scale screening of MOFs is applied to gas storage with the use of machine learning technologies. This approach is a promising trade-off between the accuracy of ab initio methods and the speed of classical approaches, strategically combined with chemical intuition. The results demonstrate that the chemical properties of MOFs are indeed predictable (stochastically, not deterministically) using machine learning methods and automated analysis protocols, with the accuracy of predictions increasing with sample size. Our initial results indicate that this methodology is promising to apply not only to gas storage in MOFs but in many other material science projects.

Metal–organic frameworks (MOFs) have been recently used as potential nanocarrier platforms in biomedical applications such as drug storage and delivery, due to their low toxicity, biodegradability, high internal surface area, widely tunable composition, high payloads and controlled drug release.

Physisorption of hydrogen in nanoporous materials offers an efficient and competitive alternative for hydrogen storage. At low temperatures (e.g. 77 K) and moderate pressures (below 100 bar) molecular H₂ adsorbs reversibly, with very fast kinetics, at high density on the inner surfaces of materials such as zeolites, activated carbons and metal–organic frameworks (MOFs). This review, by experts of Task 40 ‘Energy Storage and Conversion based on Hydrogen’ of the Hydrogen Technology Collaboration Programme of the International Energy Agency, covers the fundamentals of H₂ adsorption in nanoporous materials and assessment of their storage performance. The discussion includes recent work on H₂ adsorption at both low temperature and high pressure, new findings on the assessment of the hydrogen storage performance of materials, the correlation of volumetric and gravimetric H₂ storage capacities, usable capacity, and optimum operating temperature. The application of neutron scattering as an ideal tool for characterising H₂ adsorption is summarised and state-of-the-art computational methods, such as machine learning, are considered for the discovery of new MOFs for H₂ storage applications, as well as the modelling of flexible porous networks for optimised H₂ delivery. The discussion focuses moreover on additional important issues, such as sustainable materials synthesis and improved reproducibility of experimental H₂ adsorption isotherm data by interlaboratory exercises and reference materials.