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

2025/10/22 by Zhen Zhu, Zhu, Zhen, Shan Gao +9
Chemical Engineering · Materials Science · Chemistry · #Ammonia Synthesis and Nitrogen Reduction #Machine Learning in Materials Science #Inorganic Chemistry and Materials

paper · pdf · doi:10.48550/arxiv.2510.19343

Abstract

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 (\rm NO₃RR) performance across 286 single-atom catalysts (SACs) with the active sites anchored on double-vacancy \rm BC₃ monolayers. Through Shapley Additive Explanations (SHAP) analysis with reliable predictive accuracy, we quantitatively demonstrated that, favorable \rm NO₃RR activity stems from a delicate balance among three critical factors: low \rm N_V, moderate \rm D_N, and specific doping patterns. Building upon these insights, we established a descriptor (ψ) that integrates the intrinsic catalytic properties and the intermediate O-N-H angle (θ), effectively capturing the underlying structure-activity relationship. Guided by this, we further identified 16 promising catalysts with predicted low limiting potential (U_{\rm L}). Importantly, these catalysts are composed of cost-effective non-precious metal elements and are predicted to surpass most reported catalysts, with the best-performing Ti-V-1N1 is predicted to have an ultra-low U_{\rm L} of -0.10 V.

Citations

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