Identifying the Catalytic Descriptor of Single-Atom Catalysts in Nitrate Reduction Reaction: An Interpretable Machine-Learning Method

Elucidating the catalytic descriptor that accurately characterizes the structure–activity relationships of typical catalysts for various important heterogeneous catalytic reactions is pivotal for designing high-efficient catalytic systems. Here, an interpretable machine learning technique was employed to identify the key determinants governing the nitrate reduction reaction (NO3RR) performance across 286 single-atom catalysts (SACs) with the active sites anchored on double-vacancy BC3 monolayers. Through Shapley Additive Explanations (SHAP) analysis with reliable predictive accuracy, we quantitatively demonstrated that, favorable NO3RR activity stemmed from a delicate balance among three critical factors: low…

• NN
  National Natural Science Foundation of China

  Awards: U23A2072, 12404274, 12374061
• NS
  Natural Science Foundation of Zhejiang Province

  Awards: LQN25A040020, LD24F040001