It is worth noting that imbalanced dataset may deteriorate the performance and generalizability of machine learning model. Thus, to improve the quality of the dataset, the isostat program 51 is used to eliminate redundant points that show excessive similarity in both energy and geometry. This program uses a dual-threshold approach to screen for redundant configurations, considering both structure and energy: the configuration is retained only if it exceeds both predefined thresholds. To further improve the sampling efficiency, the generalized Euclidean distance metric is used to select points that are not too close to the existing dataset, 37