A Data Guided Approach for the Evaluation of Zeolites for Hydrogen Storage with the Aid of Molecular Simulations

Abstract

Abstract Grand Canonical Monte Carlo (GCMC) simulations have been used to evaluate the hydrogen storage performance on 233 zeolites. LTA had the highest capacity with a hydrogen capacity of 4.8%wt. The second ranked zeolite is JBW with a hydrogen uptake capacity of 3.25% wt, while RTH was the third with 2.89% wt. A machine learning algorithm was used to rank the importance of various structural features such as mass (M), density (D), helium void fraction (HVF), accessible pore volume (APV), gravimetric surface area (GSA), and largest overall cavity diameter (Di) and how they affect the capacity of the zeolites. The results show that Di, D and M have a negative effect on the percentage weight capacity, while GSA and VSA have the highest positive contribution to the percentage weight. From this, the best material could be achieved by reducing the mass and density while increasing both gravimetric and volumetric surface area. Further quantum chemical calculations were also performed to calculate the adsorption energy, global reactivity electronic descriptors, and natural bond orbital analysis in order to provide insights into the interaction of the zeolites with hydrogen. This study therefore, provides new insights into the factors that affect their hydrogen storage capacity by exhibiting the importance of considering multiple factors when evaluating the performance of zeolites and demonstrates the potential of combining different computational methods to provide a more comprehensive understanding of materials.