Machine-learning-assisted design of cathode catalysts for metal-sulfur/oxygen/carbon dioxide batteries
Source abstract
Abstract
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.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Machine Learning in Materials Science · Electrocatalysts for Energy Conversion · Advancements in Battery Materials
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (100+)
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