Interpretable Machine Learning‐Assisted High‐Throughput Screening for Understanding NRR Electrocatalyst Performance Modulation between Active Center and C‐N Coordination

Abstract

Understanding the correlation between the fundamental descriptors and catalytic performance is meaningful to guide the design of high‐performance electrochemical catalysts. However, exploring key factors that affect catalytic performance in the vast catalyst space remains challenging for people. Herein, to accurately identify the factors that affect the performance of N2 reduction, we apply interpretable machine learning (ML) to analyze high‐throughput screening results, which is also suited to other surface reactions in catalysis. To expound on the paradigm, 33 promising catalysts are screened from 168 carbon‐supported candidates, specifically single‐atom catalysts (SACs) supported by a BC3 monolayer (TM@VB/C‐Nn = 0–3‐BC3) via high‐throughput screening. Subsequently, the hybrid sampling method and XGBoost model are selected to classify eligible and non‐eligible catalysts. Through feature interpretation using Shapley Additive Explanations (SHAP) analysis, two crucial features, that is, the number of valence electrons (Nv) and nitrogen substitution (Nn), are screened out. Combining SHAP analysis and electronic structure calculations, the synergistic effect between an active center with low valence electron numbers and reasonable C‐N coordination (a medium fraction of nitrogen substitution) can exhibit high catalytic performance. Finally, six superior catalysts with a limiting potential lower than −0.4 V are predicted. Our workflow offers a rational approach to obtaining key information on catalytic performance from high‐throughput screening results to design efficient catalysts that can be applied to other materials and reactions.

Progress in Single/Multi Atoms and 2D‐Nanomaterials for Electro/Photocatalytic Nitrogen Reduction: Experimental, Computational and Machine Leaning Developments

“…Reproduced under terms of the CC-BY 4.0 license. [219] Copyright 2023, J. Sun et al, published by Wiley-VCH.…”

“…The authors determined that the gradient-boosting regression algorithm yielded accurate predictions for NRR with a low RMSE error of 0.07 eV. Sun et al [219] investigated BC 3 -type catalysts featuring TM doping on either B or C vacancy sites, coupled with N atom doping on coordinated B/C atoms. To identify promising catalysts for NRR, the authors employed a four-step screening strategy.…”

“…b) Description of active regions and feature engineering, explained with the workflow of machine learning. Reproduced under terms of the CC-BY 4.0 license [219]. Copyright 2023, J.…”