Molecular modelling and machine learning for high-throughput screening of metal-organic frameworks for hydrogen storage

ABSTRACT

Hydrogen is an appealing energy storage solution for electric vehicles due to its low environmental impact and faster recharge times compared to batteries. However, there are many engineering challenges involved in safely storing a sufficient amount of hydrogen onboard a vehicle with a reasonable volumetric density. Nanoporous materials such as metal–organic frameworks (MOFs) have the potential to store hydrogen at high density and only moderate pressure. Considerable research has been devoted to finding new MOFs for hydrogen storage in recent years; however, a MOF that provides sufficient hydrogen density and is suitable to commercial applications has not yet been found. Much of this research makes use of molecular modelling to screen thousands of materials in a high-throughput way. Computational screening can be an effective tool for gaining insight into structure-performance relationships as well as finding specific candidates for an application. Recently, some research groups have also used machine learning to analyze data more effectively and accelerate the screening process. In this review, we discuss some recent advances in using molecular modelling and machine learning to find materials for hydrogen storage. We also discuss and compare some popular models for the hydrogen molecule and the accuracy of different equations of state, which are important considerations for accurate molecular simulations.

KEYWORDS:

• Metal-organic frameworks
• hydrogen storage
• machine learning
• nanoporous materials

Acknowledgements

This work was supported by the U. S. Department of Energy under Award DE-FG02-08ER15967.

Disclosure statement

R.Q.S. has a financial interest in the start-up company NuMat Technologies, which is seeking to commercialize metal-organic frameworks.

Additional information

Funding

This work was supported by U.S. Department of Energy: [Grant Number DE-FG02-08ER15967].