Machine Learning Driven High‐Throughput Screening of Asymmetric Dinuclear Cobalt for Nitrate‐to‐Ammonia Reduction with Near‐100% Selectivity

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…

• SS
  Salt Science Research Foundation
• NN
  National Natural Science Foundation of China

  Awards: 22368012, 22106166