Energy-based descriptors to rapidly predict hydrogen storage in metal–organic frameworks

Bucior, Benjamin J.; Bobbitt, N. Scott; Islamoglu, Timur; Goswami, Subhadip; Gopalan, Arun; Yildirim, Taner; Farha, Omar K.; Bagheri, Neda; Snurr, Randall Q.

doi:10.1039/C8ME00050F

Energy-based descriptors to rapidly predict hydrogen storage in metal–organic frameworks

Journal Article · Thu Nov 01 00:00:00 UTC 2018 · Molecular Systems Design & Engineering

DOI:https://doi.org/10.1039/C8ME00050F· OSTI ID:1508085

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Northwestern Univ., Evanston, IL (United States); Nanoporous Materials Genome Center
Northwestern Univ., Evanston, IL (United States)
National Inst. of Standards and Technology (NIST), Gaithersburg, MD (United States)

The low volumetric density of hydrogen is a critical limitation to its use as a transportation fuel. Filling a fuel tank with nanoporous materials, such as metal–organic frameworks (MOFs), could greatly improve the deliverable capacity of these tanks if appropriate materials could be found. Yet, since MOFs can be made from many combinations of metal nodes, organic linkers, and functional groups, the design space of possible MOFs is enormous. Experimental characterization of thousands of MOFs is infeasible, and even conventional molecular simulations can be prohibitively expensive for large databases. In this work, we have developed a data-driven approach to accelerate materials screening and learn structure–property relationships. We report new descriptors for gas adsorption in MOFs derived from the energetics of MOF–guest interactions. Using the bins of an energy histogram as features, we trained a sparse regression model to predict gas uptake in multiple MOF databases to an accuracy within 3 g L^-1. The interpretable model parameters indicate that a somewhat weak attraction between hydrogen and the framework is ideal for cryogenic storage and release. Our machine learning method is more than three orders of magnitude faster than conventional molecular simulations, enabling rapid exploration of large numbers of MOFs. As a case study, we applied the method to screen a database of more than 50 000 experimental MOF structures. We experimentally validated one of the top candidates identified from the accelerated screening, MFU-4l. This material demonstrated a hydrogen deliverable capacity of 47 g L^-1 (54 g L^-1 simulated) when operating at storage conditions of 77 K, 100 bar and delivery at 160 K, 5 bar.

View Accepted Manuscript (DOE)

Research Organization:: Univ. of Minnesota, Minneapolis, MN (United States). Nanoporous Materials Genome Center

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES); USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22)

Grant/Contract Number:: SC0008688

OSTI ID:: 1508085

Alternate ID(s):: OSTI ID: 1481431

Journal Information:: Molecular Systems Design & Engineering, Journal Name: Molecular Systems Design & Engineering Journal Issue: 1 Vol. 4; ISSN 2058-9689; ISSN MSDEBG

Publisher:: Royal Society of ChemistryCopyright Statement

Country of Publication:: United States

Language:: English

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