Research in hydrogen storage (as opposed to containment) systems has been focusing on two classes of hydrogen storage materials: chemisorption and physisorption materials. In chemisorption materials, hydrogen is stored in chemical form as hydrides, and the release of hydrogen requires energy input. However, in physisorption materials, hydrogen is stored as molecular hydrogen, which is weakly bonded within the material, and, therefore, can be stored and released at a lesser energy cost. Finding physisorption materials is currently a critical bottleneck in the H-economy roadmap. Recent reports have applied a combination of atomistic computational methods and machine learning (ML) for identifying metallic clusters and metal-organic frameworks with high H2 adsorption capacity.
This project will focus on two-dimensional (2D) materials as potential H2 storage structures, owing to their large adsorption surface area for H2 adsorption and the feasibility of their chemical modification. The key milestones of the project will include: (1) the establishment of material features (featurisation) to map the 2D material structure against the H2 adsorption strength, capacity and reversibility, and (2) the application of ML on the said features to rapidly screen the current publicly-available databases of >6,000 2D materials to identify stable materials with H2 adsorption capacity of 7.5 wt%5 and adsorption energy per H2 molecule ~ -0.16 eV.
This project is expected to deliver a database of novel materials that can be employed in IFM's collaboration with Hycel. We propose that density functional theory calculations, with van der Waals correction, can be used for the calculation of the H2 adsorption, and an ab initio molecular dynamics approach (Born-Oppenheimer and/or Car-Parrinello MD) be used to examine stability of H2 in response to applied temperature. Pressure should also be incorporated as a variable in the calculations. ML models will be trained on datasets of calculated adsorption energies which can then be extrapolated for identifying novel adsorption surfaces.
This project is entirely focused on modeling and machine learning how to identify and discover new hydrogen storage materials. It is completely aligned with IFM's core area of Materials for the Hydrogen Economy.
This scholarship is available over 3 years.
- Stipend of $28,900 per annum tax exempt (2022 rate)
- Relocation allowance of $500-1500 (for single to family) for students moving from interstate
To be eligible you must:
- Be either a domestic candidate currently residing in Australia. Domestic includes candidates with Australian Citizenship, Australian Permanent Residency or New Zealand Citizenship.
- Meet Deakin's PhD entry requirements
- Be enrolling full time and hold an honours degree (first class) or an equivalent standard master's degree with a substantial research component.
Please refer to the research degree entry pathways page for further information.
For more information about this scholarship, please contact Dr Sherif Abbas
Dr Sherif Abbas
+61 3 522 73587