Artificial Intelligence Optimizations for High Performance Zinc-Air Batteries

Abstract

Zinc-air batteries (ZABs) are considered one of the most promising energy storage devices due to their simple structure, abundant zinc resources, excellent performance, and environmental friendliness. However, ZABs still face numerous challenges, such as zinc dendrite formation, low catalytic activity of the air electrode, severe electrode polarization, and poor electrolyte stability. To address these issues, artificial intelligence (AI) technology is being widely applied in material design, performance prediction, and experimental simulation, driving the intelligent development of ZABs. This paper reviews the working mechanism of ZABs, key components including zinc anodes, electrolytes, and air electrodes, as well as existing performance enhancement technologies; it also reviews AI technologies such as machine learning (ML) and deep learning (DL) and delves into the latest application advancements of AI in the ZAB field. The study highlights that AI-driven materials informatics will become the core driving force for the development of high-performance ZABs, and future efforts must address challenges such as data scarcity and multi-scale modeling.