Screening of Silver‐Based Single‐Atom Alloy Catalysts for NO Electroreduction to NH3 by DFT Calculations and Machine Learning

Exploring NO reduction reaction (NORR) electrocatalysts with high activity and selectivity toward NH3 is essential for both NO removal and NH3 synthesis. Due to their superior electrocatalytic activities, single‐atom alloy (SAA) catalysts have attracted considerable attention. However, the exploration of SAAs is hindered by a lack of fast yet reliable prediction of catalytic performance. To address this problem, we comprehensively screened a series of transition‐metal atom doped Ag‐based SAAs. This screening process involves regression machine learning (ML) algorithms and a compressed‐sensing data‐analytics approach parameterized with density‐functional inputs. The results demonstrate that Cu/Ag and Zn/Ag can efficiently activate and hydrogenate NO with small Φmax(η), a grand‐canonical adaptation of the Gmax(η) descriptor, and exhibit higher affinity to NO over H adatoms to suppress the competing hydrogen evolution reaction. The NH3 selectivity is mainly determined by the s orbitals of the doped single‐atom near the Fermi level. The catalytic activity of SAAs is highly correlated with the local environment of the active site. We further quantified the relationship between the intrinsic features of these active sites and Φmax(η). Our work clarifies the mechanism of NORR to NH3 and offers a design principle to guide the screen of highly active SAA catalysts. Based on the four filter criteria in a stepwise manner, Cu/Ag and Zn/Ag were screened out as promising NORR to NH3 SAA catalysts. Utilizing the database constructed by DFT calculations, we developed a machine learning model to predict the catalytic performance of SAAs and rank the crucial features. SISSO was further employed to quantitatively verify the relationships between these important features and the Φmax(η) descriptor.