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We present a fast and accurate, semi-analytical method for predicting hydrogen adsorption in nanoporous materials. For any temperature and pressure, the adsorbed amount is calculated as an integral over the energy density of adsorption sites (guest-host interactions) plus an average guest-guest term. The guest-host interaction energy is calculated using a classical force field with hydrogen modelled as a single-site probe. The guest-guest interaction energy is approximated using an average coordination number, which is regressed using Gaussian Process Regression (GPR). Local adsorption at each site is then modelled using a Langmuir isotherm, which when weighted with its probability density gives an accurate description of hydrogen adsorption. The method is tested on 933 metal-organic frameworks (MOFs) from the Computation-Ready Experimental (CoRE) MOF database at 77 K from 10 − 5 to 100 bar, and the results are compared against GCMC predictions. To demonstrate the utility of the method, we calculated hydrogen adsorption isotherms for 12,914 existing MOF structures, at two different temperatures at a speed about 100 times that of GCMC simulations and analyzed the results. We found 13 MOFs with predicted deliverable capacities exceeding the DOE target of 50 g/L for adsorption at 100 bar, 77 K and desorption at 5 bar, 160 K. [ABSTRACT FROM AUTHOR]

Hydrogen is an appealing energy storage solution for electric vehicles due to its low environmental impact and faster recharge times compared to batteries. However, there are many engineering challenges involved in safely storing a sufficient amount of hydrogen onboard a vehicle with a reasonable volumetric density. Nanoporous materials such as metal–organic frameworks (MOFs) have the potential to store hydrogen at high density and only moderate pressure. Considerable research has been devoted to finding new MOFs for hydrogen storage in recent years; however, a MOF that provides sufficient hydrogen density and is suitable to commercial applications has not yet been found. Much of this research makes use of molecular modelling to screen thousands of materials in a high-throughput way. Computational screening can be an effective tool for gaining insight into structure-performance relationships as well as finding specific candidates for an application. Recently, some research groups have also used machine learning to analyze data more effectively and accelerate the screening process. In this review, we discuss some recent advances in using molecular modelling and machine learning to find materials for hydrogen storage. We also discuss and compare some popular models for the hydrogen molecule and the accuracy of different equations of state, which are important considerations for accurate molecular simulations. [ABSTRACT FROM AUTHOR]