Exploring Feature Engineering for Crystal Structure Classification: Interactive Applications of PCA and PLS-DA Clustering

• Danila Shiryaev ^[1] ; Balaranjan Selvaratnam ^[1] ; Yujing Sun ^[2] ; Emil I. Jaffal ^[1] ; Anton O. Oliynyk ^[1]
  1. [1] Hunter College

     Hunter College

     Estados Unidos

     
  2. [2] Bronx High School of Science

     Bronx High School of Science

     Estados Unidos

     
• Localización: Journal of chemical education, ISSN 0021-9584, Vol. 103, Nº 1, 2026
• Idioma: inglés
• Texto completo no disponible (Saber más ...)
• Resumen

  • Active learning through interactive exploration significantly enhances student engagement and understanding of chemistry. This educational activity demonstrates Principal Component Analysis (PCA) and Partial-Least-Square-Discriminant Analysis (PLS-DA), two foundational machine learning techniques widely applied in contemporary research. Interactive Python-based programs offer accessible educational platforms for students exploring chemical data, requiring no prior programming experience. This application allows learners to actively engage in feature exploration and dimensionality reduction processes, applied to clustering and classifying binary AB equiatomic solid state compounds. Students can actively select and modify chemical and physical features, observing in real time how these choices impact the effectiveness of the PCA and PLS-DA clustering models. Initially, PCA enables unsupervised visualization of natural clustering and correlations among compounds without prior labeling. Subsequently, by employing PLS-DA, students develop supervised models capable of predicting crystal structures, explicitly illustrating supervised versus unsupervised learning paradigms. The activity highlights the importance of explainability in machine learning models rather than operating the models as a ″black box″. Beyond learning fundamental concepts, the activity encourages students to participate in genuine exploratory processes, mirroring the investigative approaches historically utilized by researchers and practiced today. By experimenting freely with data sets and computational methods, students experience firsthand the iterative nature of scientific discovery, fostering deeper insight into both chemical informatics and the broader research methodology.