Magnesium air batteries in terms of machine learning, anode alloying, electrolyte and air-cathode - A review

Mg-air batteries, as a new energy storage solution, exhibit enormous potentials due to their high energy density and simple structure. However, traditional designs of Mg-air batteries still face theoretical, cost-related and time-consuming limitations. The integration of machine learning (ML) and density functional theory (DFT) presents a promising approach to optimize anode electrode and battery reaction kinetics. This review provided an overview of the fundamental principles of Mg-air batteries, focusing on aspects including ML/DFT-assisted design, anode alloying, electrolyte, and cathode catalysts. We reviewed recent research progress on each of these components, highlighted the primary challenges and summarized the directions of future developments for Mg-air batteries. Finally, we offered insights for improving the performance and commercial viability of Mg-air batteries.