Data Driven Discovery of MOFs for Hydrogen Gas Adsorption

Hydrogen gas (H2) is a
clean and renewable energy source,
but the lack of efficient and cost-effective storage materials is
a challenge to its widespread use. Metal–organic frameworks
(MOFs), a class of porous materials, have been extensively studied
for H2 storage due to their tunable structural and chemical
features. However, the large design space offered by MOFs makes it
challenging to select or design appropriate MOFs with a high H2 storage capacity. To overcome these challenges, we present
a data-driven computational approach that systematically designs new
functionalized MOFs for H2 storage. In particular, we showcase
the framework of a hybrid particle swarm optimization integrated genetic
algorithm, grand canonical Monte Carlo (GCMC) simulations, and our
in-house MOF structure generation code to design new MOFs with excellent
H2 uptake. This automated, data driven framework adds appropriate
functional groups to IRMOF-10 to improve its H2 adsorption
capacity. A detailed analysis of the top selected MOFs, their adsorption
isotherms, and MOF design rules to enhance H2 adsorption
are presented. We found a functionalized IRMOF-10 with an enhanced
H2 adsorption increased by ∼6 times compared to
that of pure IRMOF-10 at 1 bar and 77 K. Furthermore, this study also
utilizes machine learning and deep learning techniques to analyze
a large data set of MOF structures and properties, in order to identify
the key factors that influence hydrogen adsorption. The proof-of-concept
that uses a machine learning/deep learning approach to predict hydrogen
adsorption based on the identified structural and chemical properties
of the MOF is demonstrated.