Machine-learning-assisted design of cathode catalysts for metal-sulfur/oxygen/carbon dioxide batteries

Metal-sulfur/oxygen/carbon dioxide batteries, which are promising high-energy power systems, all suffer from the drawback of slow reaction kinetics in cathode reactions, resulting in suboptimal battery performance. Cathode catalysts can effectively accelerate reaction kinetics, thereby enhancing battery performance. However, challenges remain in catalyst screening, and there is an unclear understanding of catalytic mechanisms. Machine learning offers a rapid approach to screening efficient catalysts and deeply exploring the mechanism of catalysis, making it a promising tool for advancing catalyst development. Nowadays, comprehensive reviews on the role of machine learning in aiding the development of cathode catalysts for metal-sulfur/oxygen/carbon dioxide batteries are rare. This review systematically summarizes the application of machine learning in cathode catalysts and presents some perspectives for future research. This review may be useful for developing Metal-sulfur/oxygen/carbon dioxide batteries and related areas. A systematic review with regard to application of machine learning in the screening of cathode catalysts for metal-sulfur/oxygen/carbon dioxide battery systems. Due to its efficiency and the ability to establish relationships between input data and outputs, machine learning can be used to deeply explore the reaction mechanisms of catalysts. Currently, machine learning has been applied to various cathode catalysts, including transition metal catalysts, single-atom catalysts, dual-metal site catalysts, and alloy catalysts, and so on cathode catalyst, machine learning, metal-sulfur/oxygen/carbon dioxide battery, review and perspective.