Molecular Dynamics Simulations of (De)Hydrogenation Mechanisms of Solid-State Hydrogen Storage Materials

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Because of the high gravimetric density of hydrogen (7.7 wt%), magnesium hydride (MgH2) is one of the most studied material for solid state hydrogen storage applications. However, the poor kinetics of the hydrogenation and dehydrogenation reactions have hindered its performance in practical applications. Experimentally, it is very difficult to characterise what is happening inside the materials, i.e. Mg/MgH2, during the hydrogenation and dehydrogenation reactions. This project aims to develop accurate neural networks (NN) potentials for the Mg-H system. The NN potentials will be trained from ab initio calculations. These NN potentials will then be used for large-size and long-timescale molecular dynamics simulations, in order to understand the atomistic mechanisms of key steps involved in the hydrogenation and dehydrogenation reactions. Based on the fundamental understanding, we will design new materials with improved kinetics and good thermodynamics for solid state hydrogen storage applications.