Machine Learning Accelerated Discovery of Entropy-Stabilized Oxide Catalysts for Catalytic Oxidation.

The catalytic properties of unary to ternary metal oxides were already well experimentally explored, and the left space seems like only high entropy metal oxides (HEOs, element types ≥5). However, the countless element compositions make the trial-and-error method of discovering HEO catalysts impossible. Herein, based on the study of the crystal phase and catalytic performance of the ACr₂O_x catalyst system, the strong correlation between the single spinel phase and good catalytic activity of CH₄ oxidation was inferred owing to the similar element importance sequences, which were acquired by the corresponding high accuracy machine learning models (cross-validation score >0.7). Furthermore, searching for negative data and choosing the proper training data resulted in high-quality regression models to search for better catalysts. Finally, the screened irregular catalyst Ni_0.04Co_0.48Zn_0.36V_0.12Cr₂O_x with outstanding sulfur and moisture resistance and long-term stability (>7000 h, T₉₀ = 345 °C) envisions the potential of applying the machine learning method to discover HEOs for target processes.