Machine learning-based high-throughput screening of Mg-containing alloys for hydrogen storage and energy conversion applications

• DOI: 10.1016/j.est.2023.107720
• Publication Date: 2023-05-20T11:13:35Z
• Authors: Katarina Batalović; Jana Radaković; Bojana Kuzmanović; Mirjana Medić Ilić; Bojana Paskaš Mamula
• Keywords: Gravimetric analysis

The development of novel materials for hydrogen storage and conversion applications is expected to facilitate the transition to clean energy. In particular, near-ambient hydrogen storage, thermal energy storage, and lithium conversion electrodes are selected in this study as the applications for which the development of novel Mg-containing materials is of great importance. We utilize a machine learning model, based on the graph neural network, developed for predicting hydride formation enthalpy in intermetallic compounds, to perform high-throughput screening based on the atomic composition and crystal structure of the starting intermetallic compounds. Trends and structure-property relations are discussed, as well as the possibilities for tailoring the stability of Mg-containing hydrides by alloying. For 636 compounds identified as stable by DFT calculations, we predict hydride formation enthalpy and equilibrium potential of metal hydride conversion electrode for Li-ion batteries. Based on the predicted enthalpy of hydride formation, 32 intermetallics are identified as suitable for near-ambient hydrogen storage applications. Among them, MgBe13, seen as a promising material to achieve a high gravimetric density of hydrogen, is additionally studied using DFT. Further investigation of the Na-Mg-Al alloys is proposed as a good route in the search for new thermal energy storage materials. Binary Mg-containing intermetallics are discussed as conversion-type negative electrodes in Li-ion batteries.