CO Oxidation Catalyzed by Perovskites: The Role of Crystallographic Distortions Highlighted by Systematic Experiments and Artificial Intelligence

The identification of key materials' parameters correlated with the performance can accelerate the development of heterogeneous catalysts and unveil the relevant underlying physical processes. However, the analysis of correlations is often hindered by inconsistent data. Besides, nontrivial, yet unknown relationships may be important, and the intricacy of the various processes may be significant. Here, we tackle these challenges for the CO oxidation catalyzed by perovskites using a combination of rigorous experiments and artificial intelligence. A series of 13 ABO₃ (A=La, Pr, Nd, Sm; B=Cr, Mn, Fe, Co) perovskites was synthesized, characterized, and tested in catalysis. To the resulting dataset, we applied the symbolic-regression SISSO approach. We identified an analytical expression correlated with the activity that contains the normalized unit-cell volume, the Pauling electronegativity of the elements A and B, and the ionization energy of the element B. Therefore, the activity is described by crystallographic distortions and by the chemical nature of A and B elements. The generalizability of the identified descriptor is confirmed by the good quality of the predictions for 3 additional ABO₃ and 16 chemically more complex AMn_(1-x)B'_xO₃ (A=La, Pr, Nd; B'=Fe, Co, Ni, Cu, Zn) perovskites.