Application and feasibility analysis of random Forest models in safety prediction for transition metal-doped Na3V2(PO4)3 sodium-ion batteries.

; infrared spectroscopy reveals minimal temperature variation during charge-discharge cycles. Accelerated Calorimetry (ARC), Thermogravimetric Analysis (TG) and Differential Scanning Calorimetry (DSC) tests further confirmed that element doping significantly improves the thermal stability of NVP, increases heat absorption and reduces the risk of thermal runaway. Building on this, a Random Forest (RF) machine learning model was developed to systematically analyze thermal runaway data and predict safety performance under different doping conditions. Model evaluation results indicate that the RF model possesses high predictive accuracy and good generalisation ability, enabling it to effectively identify key features influencing safety. Integrating experimental and modelling results, the combination of transition metal element doping modification and machine learning prediction methods provides a new technical pathway and theoretical basis for the design and feasibility assessment of high-safety sodium-ion batteries.