Machine‐learning‐aided Au‐based single‐atom alloy catalysts discovery for electrochemical NO reduction reaction to NH 3

Abstract Direct electrochemical conversion of NO to NH 3 has attracted widespread interest as a green and sustainable strategy for both ammonia synthesis and nitric oxide removal. However, designing efficient catalysts remains challenging due to the complex reaction mechanism and competing side reactions. Single‐atom alloy (SAA) catalysts, which increase the atomic efficiency and the chance to tailor the electronic properties of the active center, have become a frontier in this field. Here, we performed a systematic screening of transition metal‐doped Au SAAs (denoted as TM/Au, TM = Sc, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Ru, Rh, Pd, Ag and Pt) to find potential catalysts for electrochemical NO reduction reaction (NORR) to NH 3 . By employing a four‐step screening strategy based on density functional theory (DFT) calculations, Zn/Au SAA has been identified as a promising NORR catalyst due to its superior structural stability, reaction activity and NH 3 selectivity. The electron‐involved steps on Zn/Au are thermodynamically spontaneous, which results in a positive limiting potential ( U L ) of 0.15 V. The preferred NO affinity compared to H adatom demonstrates that Zn/Au can effectively suppress the hydrogen evolution reaction. Machine‐learning (ML) investigations were adopted to address the uncertainty between the physicochemical properties of SAAs and the NORR performance. We applied an extreme gradient boosting regression (XGBR) algorithm to predict the limiting potentials in terms of the intrinsic features of the reaction site. The coefficient of determination ( R 2 ) is 0.97 for the training set and 0.96 for the test set. The electronic structure analysis combined with a compressed‐sensing data‐analytics approach further quantitatively verifies the coeffect of d‐band center, charge transfer and the radius of doped TM atoms, i.e., features with the highest level of importance determined by the XGBR algorithm. This work provides a theoretical understanding of the complex NORR to NH 3 mechanisms and sheds light on the rational design of SAA catalysts by combining DFT and ML investigations.