Principal component analysis of catalytic functions in the composition space of heterogeneous catalysts

The objective of this paper is to identify which combinations of constituents of heterogeneous catalysts are related to which final products from a large combinatorially generated dataset. To this end, data on the composition of catalysts are combined with a set of four catalytic activities, and analyzed using a Principle Component Analysis (PCA). From the first attempt of PCA, a dataset of pentanary-mixed metal oxides, Cr_aMo_bNi _c- Co_dMn_eO_x, is visualized on the planes of principal components (PCs) in a way of linking the composition spread with the activities toward each product. It has been possible to identify three key elements (Mo, Co, and Cr) out of five by PCA. Having these key elements identified from PCA, a ternary system of mixed metal oxide catalysts, Mo _aCo_bCr_cO_x, was analyzed again in the PC space for further relationships between composition of catalysts and activities of final products. These two case studies demonstrate how large multidimensional datasets can be analyzed to identify relevant key parameters for catalyst discovery and optimization.