Stabilization of Na‐Ion Cathode Surfaces: Combinatorial Experiments with Insights from Machine Learning Models

Abstract

Na–Fe–Mn–O cathodes hold promise for environmentally benign high‐energy sodium‐ion batteries, addressing material scarcity concerns in Li‐ion batteries. To date, these materials show poor stability in the air and suffer significant Fe/Mn dissolution during use. These two detrimental surface effects have so far prevented the commercialization of these materials. Herein, high‐throughput experiments to make hundreds of substitutions into a previously optimized Na–Fe–Mn–O material are utilized. Numerous single‐phase materials are made with good electrochemical performance that shows moderate improvements over the unsubstituted. By contrast, dramatic improvements are made in suppressing decomposition in air and Fe/Mn dissolution. Machine learning algorithms are utilized to further understand the changes in air stability and to decouple the effects of various structural parameters such as lattice parameters and crystallite size. The comprehensive dataset and methodology established here lay the groundwork for future exploration and optimization of cathode materials, driving the advancement of next‐generation sodium‐ion batteries.

Keywords

• air stability
• high‐throughput experimentation
• machine learning algorithms
• Na‐ion battery cathodes
• transition metal dissolution