Abstract
The low volumetric density of hydrogen is a major 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. However, 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 50000 experimental MOF structures. We experimentally validated one of the top candidates identified from the accelerated screening, MFU-4l. This material exhibited 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.
Original language | English (US) |
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Pages (from-to) | 162-174 |
Number of pages | 13 |
Journal | Molecular Systems Design and Engineering |
Volume | 4 |
Issue number | 1 |
DOIs | |
State | Published - Feb 2019 |
Funding
This research was supported by the U.S. Department of Energy, Office of Basic Energy Sciences, Division of Chemical Sciences, Geosciences and Biosciences under Award No. DE-FG02-17ER16362. B. J. B. also acknowledges a research grant through the Data Science Initiative at Northwestern University and a National Science Foundation Graduate Research Fellowship under Grant No. DGE-1324585. This research was supported in part through the computational resources and staff contributions provided for the Quest high-performance computing facility at Northwestern University, which is jointly supported by the Office of the Provost, the Office for Research, and Northwestern University Information Technology.
ASJC Scopus subject areas
- Chemistry (miscellaneous)
- Chemical Engineering (miscellaneous)
- Biomedical Engineering
- Energy Engineering and Power Technology
- Process Chemistry and Technology
- Industrial and Manufacturing Engineering
- Materials Chemistry