Machine Learning-based Comprehensive Survey on Lithium-rich Cathode Materials

The practical application of Li-rich cathode materials exhibiting higher energy density with oxygen redox activity requires improved cycle performance and efficiency. Since several conditions such as the amount excess lithium, transition metal species, cutoff voltage influence electrochemical properties materials, comprehensive determination optimal often rely on repeating empirical try error processes. Here, dominant factors in were analyzed by constructing machine learning prediction models based well-controlled experimental data for simplicity. Choosing a moderate lithium increasing cobalt contents are keys to achieve high long-term cycles.