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

Abstract

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_xH_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_xH_y sheets in low-energy periodic phases were identified via density functional theory (DFT) calculations; this revealed a previously unknown phase of 2D MgH₂ with a Pm2 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 MgH₂ has an expected theoretical gravimetric density of 6 wt%, with an average H₂ adsorption energy of −0.105 eV. Therefore, it is anticipated that Pm2 MgH₂ 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.