Executive Summary : | Energy is crucial for human life, and hydrogen as a fuel seems to be a promising alternative due to its potential to reduce global warming and climate change. However, the use of hydrogen in industrial applications is limited by its economical storage and transportation limitations. Solid-state hydrogen storage is preferred over liquid or gas storage. The search for a suitable alloy that meets economic viability standards is ongoing. Some metals can easily bind with hydrogen and form metal hydrides, which can be used to store hydrogen. However, recovering stored hydrogen is challenging due to the high stability of these hydrides. To find a suitable alloy, machine/statistical learning models are proposed. This project aims to build ML-based models to predict gravimetric capacity, H2wt%, and Pressure-Composition-Temperature (PCT) diagrams, as well as time taken for absorption and desorption of hydrogen at specific temperatures and pressures. To design an alloy suitable for solid-state hydrogen storage, the researchers will develop various ML-based models to predict metal alloys' thermodynamic and kinetic properties and test their usability in storing hydrogen. Validating predictions through experiments is essential, as not all models are useful. The researchers will synthesize these alloys and measure their hydrogen storage capacity to determine the usefulness of these models. |