Machine Learning Driven High‐Throughput Screening of Asymmetric Dinuclear Cobalt for Nitrate‐to‐Ammonia Reduction with Near‐100% Selectivity
Jinyu Wang, Xuxin Kang, Zhaoqin Chu, Pengfei Wu, Zihao Chen, Xiangmei Duan, Yanming Li, Quanzhen Huang, Lingling Guo, Weiyou Chen, Degao Wang, Zhifang Chai
Abstract
ABSTRACT The electrocatalytic nitrate reduction reaction (NO 3 RR) offers a promising way to reduce pollutants and synthesis ammonia. However, it is fraught with problems such as unbalanced adsorption–desorption of intermediates, insufficient H* supply, and competing hydrogen evolution. Dual‐atom catalysts (DACs) have emerged as promising candidates, but their rational design faces obstacles in balancing element selection and performance. To address this, we employed high‐throughput calculations and machine learning to screen a series of DACs for NO 3 RR. , d m1‐dm2 and were identified as key features. Based on interpretable SHAP analysis, we predicted and developed a sulphur‐doped, asymmetric, dinuclear cobalt catalyst (Co 2 /NSC). Experiments show that this catalyst achieves a Faradaic efficiency of 99.95% for NH 3 production at −0.2 V vs. RHE, with a maximum yield of 101.19 mg h −1 mgcat −1 at −0.4 V vs. RHE. The underlying mechanism and DFT calculations indicate that dinuclear Co sites enhance *NO 3 − adsorption and reduce the energy barrier of the rate‐determining step *NO→*NHO; sulphur doping facilitates active *H supply and stabilizes the *NHO intermediate via an S─H/O─H hydrogen bonding network, suppressing side reactions. In addition, the integrated membrane electrode assembly system and the technical‐economic analysis confirm the practical applicability and economic viability of this process.