Computational discovery of hydrogen storage materials
Hydrogen is one of the most promising candidates for the replacement of the current carbon-based energy sources. For this reason, both the government and the private sector are investing in the growth of hydrogen-based infrastructure. Research on efficient hydrogen storage is crucial for transportation and industrial usage since hydrogen suffers from a low energy density per volume at ambient conditions. In this project DFT, molecular simulations, and machine learning techniques are used to investigate and screen nano-porous materials and 2D materials such as Borophene, Graphene, or Metal-Organic Frameworks (MOFs). This project is a collaboration between the Engineering Thermodynamics group (Process & Energy department) and the Computational Materials Science group (Materials Science and Engineering department) within the 3mE faculty.