High quality large‐scale nickel‐rich layered oxides precursor co‐precipitation via domain adaptation‐based machine learning

Abstract Nickel‐rich layered oxides (LiNi x Co y Mn z O 2 , NCM) are among the most promising cathode materials for high‐energy lithium‐ion batteries, offering high specific capacity and output voltage at a relatively low cost. However, industrial‐scale co‐precipitation presents significant challenges, particularly in maintaining particle sphericity, ensuring a stable concentration gradient, and preserving production yield when transitioning from lab‐scale compositions. This study addresses a critical issue in the large‐scale synthesis of nickel‐rich NCM ( x = 0.8381): nickel leaching, which compromises particle uniformity and battery performance. To mitigate this, we optimize the reaction process and develop an artificial intelligence‐driven defect prediction system that enhances precursor stability. Our domain adaptation based machine learning model, which accounts for equipment wear and environmental variations, achieves a defect detection accuracy of 97.8% based on machine data and process conditions. By implementing this approach, we successfully scale up NCM precursor production to over 2 tons, achieving 83% capacity retention after 500 cycles at a 1C rate. In addition, the proposed approach demonstrates the formation of a concentration gradient in the composition and a high sphericity of 0.951 (±0.0796). This work provides new insights into the stable mass production of NCM precursors, ensuring both high yield and performance reliability. image