How does Principal Component Analysis (PCA) aid in catalysis research?
PCA is a dimensionality reduction technique that helps in simplifying complex data sets while preserving the most important information. In catalysis, PCA can be used to identify patterns and correlations in data sets, such as those obtained from high-throughput screening experiments. This can lead to the identification of key factors that influence catalytic performance.
