Design of catalysts for electrochemical nitric oxide reduction to ammonia based on stacked ensemble learning

The electrocatalytic reduction of nitric oxide for ammonia synthesis (NORR) is a key green energy conversion technology. Its efficiency relies on high-performance electrocatalysts to enhance both ammonia yield ($ {{Y}}_{{\text{NH}}_{3}} $) and Faradaic efficiency ($ {{F}}_{{\text{NH}}_{3}} $). However, conventional experimental methods for screening high-activity NORR catalysts often entail high resource consumption and time costs. Machine learning combined with SHAP feature analysis was employed to establish a stacked ensemble model that integrates multiple algorithms, to allow for a systematic investigation of the key descriptors governing NORR performance based on an experimental dataset. Evaluation of eight model algorithms revealed that the Stacked-SVR model achieved an R2 of 0.9223 and an RMSE of 0.0608 for predicting $ {{Y}}_{{\text{NH}}_{3}} $ on the test set, whereas the Stacked-RF model achieved an R2 of 0.9042 and an RMSE of 0.0900 for predicting $ {{F}}_{{\text{NH}}_{3}} $. The stacked ensemble model integrates the strengths of individual algorithms and demonstrates strong NORR prediction performance while avoiding overfitting. SHAP feature analysis results revealed that the Cu content in the catalyst composition has the most significant impact on catalytic performance. Moreover, the combination of the wet chemical reduction synthesis, a carbon fiber (CF) conductive substrate, and HCl electrolyte is more favorable for enhancing catalytic activity. Additionally, moderately lowering the working potential, controlling the electrolyte volume at low to medium levels, reducing catalyst loading, and increasing electrolyte concentration were found to synergistically enhance both $ {{Y}}_{{\text{NH}}_{3}} $and $ {{F}}_{{\text{NH}}_{3}} $.