PI: Dr. Monther Alfuraidan

In this research, we propose the use of graph theory and machine learning to gain insights into factors critical to hydrogen production and storage, aiming to develop a predictive model that can analyze and optimize the structural properties of materials used for hydrogen synthesis and storage. The graph representations of chemical structures, combined with their properties, will serve as the foundation to extract essential features. These features will then be provided to machine learning algorithms to predict factors contributing to optimal interactions.