In catalysis research, k-means clustering can be applied in several ways:
Characterizing catalyst surfaces: k-means can be used to analyze surface properties of catalysts, such as surface area, pore size distribution, and active site dispersion, to identify distinct types of surfaces. Analyzing reaction kinetics: By clustering reaction rate data, researchers can identify different reaction mechanisms or pathways, which can aid in the development of more efficient catalysts. Optimizing catalyst composition: k-means can help in the design of catalyst libraries by identifying optimal compositions that maximize performance metrics like conversion rate, selectivity, and stability. Spectroscopic data analysis: Clustering spectroscopic data (e.g., Raman, IR, XPS) can reveal patterns that are related to the structure and function of catalytic materials. High-throughput screening: In combinatorial catalysis, k-means can be used to analyze large datasets from high-throughput experiments to identify promising catalyst candidates.
