Critical Factors in Computational Characterization of Hydrogen Storage in Metal–Organic Frameworks

Inconsistencies in high-pressure H2 adsorption data and a lack of comparative experiment–theory studies have made the evaluation of both new and existing metal–organic frameworks (MOFs) challenging in the context of hydrogen storage applications. In this work, we performed grand canonical Monte Carlo (GCMC) simulations in nearly 500 experimentally refined MOF structures to examine the variance in simulation results because of the equation of state, H2 potential, and the effect of density functional theory structural optimization. We find that hydrogen capacity at 77 K and 100 bar, as well as hydrogen 100-to-5 bar deliverable capacity, is correlated more strongly with the MOF pore volume than with the MOF surface area (the latter correlation is known as the Chahine’s rule). The tested methodologies provide consistent rankings of materials. In addition, four prototypical MOFs (MOF-74, CuBTC, ZIF-8, and MOF-5) with a range of surface areas, pore structures, and surface chemistries, representative of promising adsorbents for hydrogen storage, are evaluated in detail with both GCMC simulations and experimental measurements. Simulations with a three-site classical potential for H2 agree best with our experimental data except in the case of MOF-5, in which H2 adsorption is best replicated with a five-site potential. However, for the purpose of ranking materials, these two choices for H2 potential make little difference. More significantly, 100 bar loading estimates based on more accurate equations of state for the vapor–liquid equilibrium yield the best comparisons with the experiment

Critical Factors in Computational Characterization of Hydrogen Storage in Metal–Organic Frameworks

Inconsistencies in high-pressure H2 adsorption data and a lack of comparative experiment–theory studies have made the evaluation of both new and existing metal–organic frameworks (MOFs) challenging in the context of hydrogen storage applications. In this work, we performed grand canonical Monte Carlo (GCMC) simulations in nearly 500 experimentally refined MOF structures to examine the variance in simulation results because of the equation of state, H2 potential, and the effect of density functional theory structural optimization. We find that hydrogen capacity at 77 K and 100 bar, as well as hydrogen 100-to-5 bar deliverable capacity, is correlated more strongly with the MOF pore volume than with the MOF surface area (the latter correlation is known as the Chahine’s rule). The tested methodologies provide consistent rankings of materials. In addition, four prototypical MOFs (MOF-74, CuBTC, ZIF-8, and MOF-5) with a range of surface areas, pore structures, and surface chemistries, representative of promising adsorbents for hydrogen storage, are evaluated in detail with both GCMC simulations and experimental measurements. Simulations with a three-site classical potential for H2 agree best with our experimental data except in the case of MOF-5, in which H2 adsorption is best replicated with a five-site potential. However, for the purpose of ranking materials, these two choices for H2 potential make little difference. More significantly, 100 bar loading estimates based on more accurate equations of state for the vapor–liquid equilibrium yield the best comparisons with the experiment

Critical Factors in Computational Characterization of Hydrogen Storage in Metal–Organic Frameworks

Inconsistencies in high-pressure H2 adsorption data and a lack of comparative experiment–theory studies have made the evaluation of both new and existing metal–organic frameworks (MOFs) challenging in the context of hydrogen storage applications. In this work, we performed grand canonical Monte Carlo (GCMC) simulations in nearly 500 experimentally refined MOF structures to examine the variance in simulation results because of the equation of state, H2 potential, and the effect of density functional theory structural optimization. We find that hydrogen capacity at 77 K and 100 bar, as well as hydrogen 100-to-5 bar deliverable capacity, is correlated more strongly with the MOF pore volume than with the MOF surface area (the latter correlation is known as the Chahine’s rule). The tested methodologies provide consistent rankings of materials. In addition, four prototypical MOFs (MOF-74, CuBTC, ZIF-8, and MOF-5) with a range of surface areas, pore structures, and surface chemistries, representative of promising adsorbents for hydrogen storage, are evaluated in detail with both GCMC simulations and experimental measurements. Simulations with a three-site classical potential for H2 agree best with our experimental data except in the case of MOF-5, in which H2 adsorption is best replicated with a five-site potential. However, for the purpose of ranking materials, these two choices for H2 potential make little difference. More significantly, 100 bar loading estimates based on more accurate equations of state for the vapor–liquid equilibrium yield the best comparisons with the experiment

Computation-Ready, Experimental Metal–Organic Frameworks

Experimentally refined crystal structures for metal–organic frameworks (MOFs) often include solvent molecules and partially occupied or disordered atoms. This creates a major impediment to applying high-throughput computational screening to MOFs. To address this problem, we have constructed a database of MOF structures that are derived from experimental data but are immediately suitable for molecular simulations.  The development of the CoRE MOF 2014 database is described in “Computation-ready, experimental metal-organic frameworks: A tool to enable high-throughput screening of nanoporous crystals” Chung, Y.G. et al., Chem. Mater. 2014, 26, 6185-6192 (DOI: 10.1021/cm502594j). The CoRE MOF 2014 database was developed through a collaboration of research groups participating in the Nanoporous Materials Genome Center that is supported by the U.S. Department of Energy, Office of Basic Energy Sciences, Division of Chemical Sciences, Geosciences and Biosciences under Award DE-FG02-12ER16362.</p

Computation-Ready, Experimental Metal–Organic Frameworks: A Tool To Enable High-Throughput Screening of Nanoporous Crystals

Experimentally refined crystal structures for metal–organic frameworks (MOFs) often include solvent molecules and partially occupied or disordered atoms. This creates a major impediment to applying high-throughput computational screening to MOFs. To address this problem, we have constructed a database of MOF structures that are derived from experimental data but are immediately suitable for molecular simulations. The computation-ready, experimental (CoRE) MOF database contains over 4700 porous structures with publically available atomic coordinates. Important physical and chemical properties including the surface area and pore dimensions are reported for these structures. To demonstrate the utility of the database, we performed grand canonical Monte Carlo simulations of methane adsorption on all structures in the CoRE MOF database. We investigated the structural properties of the CoRE MOFs that govern methane storage capacity and found that these relationships agree well with those derived recently from a large database of hypothetical MOFs