Unveiling two-dimensional magnesium hydride as a hydrogen storage material via a generative adversarial network

This study used an artificial intelligence (AI)-based crystal inverse-design approach to investigate the new phase of two-dimensional (2D) pristine magnesium hydride (Mg(x)H(y)) sheets and verify their availability as a hydrogen storage medium. A 2D binary phase diagram for the generated crystal images was constructed, which was used to identify significant 2D crystal structures. Then, the electronic and dynamic properties of the Mg(x)H(y) sheets in low-energy periodic phases were identified via density functional theory (DFT) calculations; this revealed a previously unknown phase of 2D MgH(2) with a P4̄m2 space group. In the proposed structure, the adsorption behaviors of the Li-decorated system were investigated for multiple hydrogen molecules. It was confirmed that Li-decorated MgH(2) has an expected theoretical gravimetric density of 6 wt%, with an average H(2) adsorption energy of −0.105 eV. Therefore, it is anticipated that P4̄m2 MgH(2) sheets can be employed effectively as a medium for hydrogen storage. Additionally, this finding indicates that a deep learning-based approach is beneficial for exploring unrevealed 2D materials.