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

Automated summary

To improve the cycle performance and energy efficiency of Li-rich cathode materials with higher energy density and oxygen redox activity, it is necessary to optimize the conditions such as excess lithium, transition metal species, and cutoff voltage. This study analyzed the dominant factors in the energy density of Li-rich cathode materials by using machine learning prediction models based on well-controlled experimental data. The results showed that choosing a moderate amount of excess lithium and increasing the cobalt contents are keys to achieving high energy density in long-term cycles.