Attainable Volumetric Targets for Adsorption-Based Hydrogen Storage in Porous Crystals: Molecular Simulation and Machine Learning

Abstract

Hydrogen fuel is attractive to power vehicles without emitting carbon, but onboard storage of sufficiently densified hydrogen at moderate pressure remains a significant challenge. Adsorption-based storage in porous crystals such as metal–organic frameworks and covalent organic frameworks is attractive to reduce the storage pressure. It is, however, unclear to what extent volumetric storage targets can be met under constraints of adsorbent design and choice of operating conditions. To help elucidate attainable values for volumetric storage metrics upon the potential introduction of strong hydrogen-binding sites, we “computationally synthesized” a library of porous crystals and performed 18 000+ grand canonical Monte Carlo simulations to calculate hydrogen loadings at multiple T, P conditions. The studied frameworks are based on 17 pore topologies and feature alchemical catecholate sites: sites whose interaction with hydrogen was artificially and systematically modified within the range of density functional theory-calculated hydrogen–catecholate binding energies found in the literature. Porous crystals with the tetrahedrally connected topologies dia and qtz tended to outperform other types of crystals for each “level” of hydrogen–alchemical site interaction strength. Among tested operating conditions, 100 bar/77 K ↔ 5 bar/160 K swing conditions produced the highest optimal deliverable capacity (62 g/L with a 10 kJ/mol heat of adsorption), which was 138% higher than that for the 100 bar ↔ 5 bar swing at ambient temperature (26 g/L with a 17 kJ/mol heat of adsorption). Porous crystals simultaneously featuring void fractions and volumetric surface areas in the 0.7–0.9 and 1300–1800 m2/cm3 ranges, respectively, were more susceptible to improvements in deliverable capacity for the 100 bar/77 K ↔ 5 bar/160 K swing by tuning their interactions with hydrogen. Select simulations were analyzed in more detail to obtain adsorption mechanism insights. Leveraging all of the generated data, we trained, for the first time, a single artificial neural network capable of predicting hydrogen loadings at multiple T, P conditions. Using this neural network, we estimated that, for the nonisothermal 77 K ↔ 160 K swing, reducing the storage pressure from 100 to 35 bar only reduces the attainable deliverable capacity to 59 g/L, which may be an acceptable trade-off due to safety and compression cost implications. As the neural network only uses simple descriptors as input, modelers and experimentalists alike could potentially use it to rapidly pre-assess the hydrogen storage capabilities of newly proposed crystal designs at various swing conditions.

Exceptional hydrogen storage achieved by screening nearly half a million metal-organic frameworks

“…Nevertheless, higher capacities can be achieved by adopting a more complex operating scheme. For example, the HSECoE has proposed an alternative operating scenario involving a combined temperature and pressure swing (TPS) from T min = 77 K, P max = 100 bar (filled) to T max = 160 K, P min = 5 bar (empty) 3,43,51,52 . By heating during desorption, the TPS approach increases capacity by minimizing the amount of H 2 retained in the MOF in the low-pressure empty state 4 .…”

The role of molecular modelling and simulation in the discovery and deployment of metal-organic frameworks for gas storage and separation

“…Motivated by the promise of metalated catechols for hydrogen storage [107], Anderson et al [108] constructed 105 MOF crystals with “alchemical” metalated catechols on their linkers. The sites are “alchemical” because the interaction of H 2 with the metalated catechol was allowed to artificially vary to account for variations in chemistry without the explicit specification of it.…”

Big-Data Science in Porous Materials: Materials Genomics and Machine Learning

“…An interesting avenue might be to combine this approach with the ideas from Anderson et al., who encode the sorbates by training with different achemical species (e.g., varying the Lennard-Jones interaction strength, ϵ). 481 , 482 …”