Data Driven Discovery of MOFs for Hydrogen Gas Adsorption

SK Singh and AT Sose and FX Wang and KK Bejagam and SA Deshmukh, JOURNAL OF CHEMICAL THEORY AND COMPUTATION, 19, 6686-6703 (2023).

DOI: 10.1021/acs.jctc.3c00081

Hydrogen gas (H-2) 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 H-2 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 H-2 storage capacity. To overcome these challenges, we present a data-driven computational approach that systematically designs new functionalized MOFs for H-2 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 H-2 uptake. This automated, data driven framework adds appropriate functional groups to IRMOF-10 to improve its H-2 adsorption capacity. A detailed analysis of the top selected MOFs, their adsorption isotherms, and MOF design rules to enhance H-2 adsorption are presented. We found a functionalized IRMOF-10 with an enhanced H-2 adsorption increased by similar to 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.

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