Machine Learning Analysis of Hydrothermally Synthesized LiFePO₄ for Lithium‐Ion Battery

ABSTRACT Rechargeable lithium‐ion batteries (LIBs) have quickly become one of the most popular energy storage sources for electronic devices. The LIB cathode significantly affects the battery's energy density, safety, lifespan, and cost, and LIBs exhibit better chemical and thermal stability. Among various cathode materials, lithium iron phosphate (LiFePO 4 ) has gained significant attention due to its excellent safety, low toxicity, cost‐effectiveness, and structural stability, making it a preferred choice for commercial and high‐performance battery applications. However, the electrochemical performance of LiFePO 4 is strongly influenced by its morphology and nanostructure. This review provides a comprehensive analysis of hydrothermally synthesized LiFePO 4 nanomaterials, focusing on their structural, morphological, and electrochemical properties. A detailed discussion of 1D, 2D, and 3D LiFePO 4 nanostructures is presented, highlighting their impact on Li‐ion transport, conductivity, and overall battery performance. Furthermore, the electronic structure of LiFePO 4 is examined for its charge storage mechanisms. A novel aspect of this review is the application of machine learning techniques to analyze specific capacity variations under different hydrothermal synthesis conditions and electrochemical parameters, offering insights into performance optimization. Finally, the global challenges, prospects, and research opportunities for LiFePO 4 ‐based LIBs are discussed, providing a roadmap for further advancements in this field.