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

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

*Corresponding author for this work

Research output: Contribution to journalArticle

1 Scopus citations

Abstract

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 (Formula presented.) 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.

Original languageEnglish (US)
JournalMolecular Physics
DOIs
StateAccepted/In press - Jan 1 2019

Keywords

  • Gaussian process regression
  • high-throughput screening
  • Hydrogen storage
  • machine learning
  • metal-organic frameworks
  • MOFs

ASJC Scopus subject areas

  • Biophysics
  • Molecular Biology
  • Condensed Matter Physics
  • Physical and Theoretical Chemistry

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