Principal component analysis (PCA) is employed to identify the directions of greatest variation in the dataset and re-express them in a new basis, effectively reducing data complexity while preserving essential characteristics. This transformation allows underlying patterns to emerge, eliminates redundancies, and minimizes noise interference. The application of PCA in scientific studies not only simplifies and organizes large datasets but also provides valuable insights into the relationships between variables, facilitating a more effective understanding of the synthesis-property interactions in CDs .