Machine Learning-based Comprehensive Survey on Lithium-rich Cathode Materials (Supporting Information)

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.

具备氧氧化还原活性、能量密度更高的富锂正极材料（Li-rich cathode materials），其实际应用亟需优化循环性能与能源利用效率。由于过量锂含量、过渡金属种类、截止电压等多种条件均会影响富锂正极材料的电化学性能，因此全面确定最优工艺条件往往需要反复开展经验性试错流程。为简化分析流程，本研究通过构建基于可控实验数据的机器学习预测模型，对影响富锂正极材料能量密度的核心因素展开了分析。研究结果表明，适度控制过量锂的添加量并提升钴元素占比，是实现长循环工况下高能量密度的关键所在。