High-Throughput Screening of MN4 Coordinated Macrocyclic Frameworks for CO Reduction by Combining First-Principles Calculations and Machine Learning

Abstract The electrochemical CO reduction reaction (CORR) by single-atom catalysts (SACs) provides a promising route for converting CO into value-added C1 products, but the rational discovery of efficient SACs remains challenging because of the large configurational space and complex reaction network. Herein, we chose two-dimensional periodic macrocyclic frameworks with MN4 coordination centers as the SACs and established a multilevel screening strategy by combining first-principles calculations and machine learning (ML). After evaluating thermodynamic stability, dissolution resistance, *CO adsorption, and hydrogen evolution reaction suppression, 90 candidates were screened out from 156 SACs for further analysis. The hydrogenation of *CO to *CHO was identified as a key step for deep CO reduction, and ΔG(*CHO-*CO) was selected as the main descriptor. An ExtraTrees regression model trained on the screened SACs achieved high predictive accuracy, with R2 values of 0.947 and 0.925 for the training and test sets, respectively. By scanning the full reaction pathway of CORR (CH4 as the final product) on the SACs with relatively low ΔG(*CHO-*CO) value, the FeC8N8 was found to exhibit the highest catalytic activity among the examined candidates, with a low limiting potential (UL) of −0.30 V. To accelerate catalyst evaluation, ΔG(*CHO-*CO), which describes the first hydrogenation of adsorbed *CO, was selected as the key descriptor and predicted by ML algorithm. Full-pathway calculations were subsequently carried out for catalysts with relatively low ΔG(*CHO-*CO) values to validate the descriptor-guided screening results.