Prediction of hydrogen adsorption in nanoporous materials from the energy distribution of adsorption sites

Gopalan, Arun; Bucior, Benjamin J.; Bobbitt, N. Scott; Snurr, Randall Q.

doi:10.1080/00268976.2019.1658910

Title: Prediction of hydrogen adsorption in nanoporous materials from the energy distribution of adsorption sites

Journal Article · Wed Aug 28 00:00:00 EDT 2019 · Molecular Physics

DOI:https://doi.org/10.1080/00268976.2019.1658910· OSTI ID:2311147

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Northwestern University, Evanston, IL (United States)

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. Here, 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.

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Research Organization:: Univ. of Minnesota, Minneapolis, MN (United States); Northwestern Univ., Evanston, IL (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES). Chemical Sciences, Geosciences & Biosciences Division (CSGB)

Grant/Contract Number:: SC0008688; FG02-17ER16362

OSTI ID:: 2311147

Journal Information:: Molecular Physics, Vol. 117, Issue 23-24; ISSN 0026-8976

Publisher:: Taylor & FrancisCopyright Statement

Country of Publication:: United States

Language:: English

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37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY
Hydrogen storage
high-throughput screening
metal-organic frameworks
MOFs
machine learning
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