Machine Learning-Assisted Discovery of Bimetallic Oxides for Highly Efficient Catalytic Ozonation

Catalytic ozonation stands out as an effective process in the advanced treatment of industrial wastewater, where heterogeneous catalysts play a pivotal role. Here, by screening 1603 bimetallic oxides via machine learning (ML), a pioneering ZnCu2O4 was dug out, validated by density-functional theory and experiments. Compared with the literature, ZnCu2O4 significantly boosted the degradation rate constant for oxalic acid (kobs = 0.30 min–1) by 1.30–61.22 times. Meanwhile, the average ozone treatment efficiency of chemical oxygen demand (COD) and total organic carbon (TOC) for high-salinity coal chemical wastewater (hsCCW), i.e., ΔCOD/ΔO3 (1.01 kg kg–1) and ΔTOC/ΔO3 (0.30 kg kg–1), reached 0.61–4.60-fold and 1.32–4.84-fold of the literature, respectively. Mechanistic studies revealed a unique nonradical pathway dominated by 1O2, ensuring resistance to environmental interference. Its particular Cu–O–Zn configuration enhanced stability and active-site exposure, which is critical for scalable applications. Overall, this research and development (R&D) framework encompassing multidimensional “theoretical calculation-machine learning-precision synthesis-mechanism elucidation” establishes a generalizable methodology for intelligent material innovation and environmental application.