Knowledge-Driven Experimental Discovery of Ce-Based Metal Oxide Composites for Selective Catalytic Reduction of NO with NH through Interpretable Machine Learning.

Mining the scientific literature, combined with data-driven methods, may assist in the identification of optimized catalysts. In this paper, we employed interpretable machine learning to discover ternary metal oxides capable of selective catalytic reduction of nitrogen oxides with ammonia (NH-SCR). Specifically, we devised a machine learning framework utilizing extreme gradient boosting (XGB), identified for its optimal performance, and SHapley Additive exPlanations (SHAP) to evaluate a curated database of 5654 distinct metal oxide composite catalytic systems containing cerium (Ce) element, with records of catalyst composition and preparation and reaction conditions. By virtual screening, this framework precisely pinpointed a CeO-MoO-FeO catalyst with superior NO conversion, N selectivity, and resistance to HO and SO, as confirmed by empirical evaluations. Subsequent characterization affirmed its favorable structural, chemical bulk properties and reaction mechanism. Demonstrating the efficacy of combining knowledge-driven techniques with experimental validation and analysis, our strategy charts a course for analogous catalyst discoveries.