Following the generation of the SOAP descriptors, we applied MinMax scaling to normalize the data, ensuring that all features were within the same range. Next, we performed Kernel Principal Component Analysis (KPCA) to reduce the dimensionality of the descriptors, retaining the top 3 components. This dimensionality reduction helped in capturing the most significant variations in the data while reducing computational complexity. For the unsupervised clustering, we employed the K-means algorithm to group the data into clusters. The optimal number of clusters was determined using silhouette score analysis, which evaluates the consistency within clusters and the separation between clusters, ensuring the most appropriate partitioning of the data (Fig. ).