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

Xiaolan Duan; Yang Li; Jiahua Zhao; Mengyuan Zhang; Xiaopeng Wang; Li Zhang; Xiaoxuan Ma; Ying Qu; Pengfei Zhang

doi:10.1021/jacs.4c12838

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

J Am Chem Soc. 2024 Oct 27. doi: 10.1021/jacs.4c12838. Online ahead of print.

Authors

Xiaolan Duan^{1

2}, Yang Li¹, Jiahua Zhao², Mengyuan Zhang², Xiaopeng Wang¹, Li Zhang¹, Xiaoxuan Ma¹, Ying Qu¹, Pengfei Zhang^{1

2}

Affiliations

¹ State Key Laboratory of High-Efficiency Utilization of Coal and Green Chemical Engineering, College of Chemistry and Chemical Engineering, Ningxia University, Yinchuan 750021, China.
² School of Chemistry and Chemical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.

PMID: 39462838
DOI: 10.1021/jacs.4c12838

Abstract

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.