Electrochemical CO_(2) reduction(CO_(2)RR)to value-added fuels and chemicals offers a promising route toward carbon neutrality.However,developing efficient and selective catalysts for the generation of multi-carbon(C^...Electrochemical CO_(2) reduction(CO_(2)RR)to value-added fuels and chemicals offers a promising route toward carbon neutrality.However,developing efficient and selective catalysts for the generation of multi-carbon(C^(2+))products remains a significant challenge.In this work,we propose a combined density functional theory(DFT)and machine learning(ML)approach to systematically screen CuSb-based catalysts with varying surface Sb atomic fractions and non-metal dopants(O,N,S,Se,and P)on the Cu_(2)Sb(100)surface for CO_(2)RR.Approximately 200 representative adsorption configurations were randomly selected for DFT calculations,which were then used to train a predictive ML model.This model enables high-accuracy predictions of the adsorption energies of key intermediates(*CO and*H)for the remaining uncalculated configurations.By integrating the K-means clustering analysis and the optimal adsorption energy selection criteria based on the Sabatier principle,the candidate configuration with the best potential for C^(2+)product formation was identified:O-doped CuSb with a surface Sb atomic fraction of 3/12.Mechanistic studies further reveal that O doping significantly strengthens *CO adsorption while suppressing *H adsorption by modulating the electronic structure,thereby lowering the CO_(2)RR energy barrier and improving the thermodynamic selectivity toward C^(2+)products.This work not only elucidates the synergistic effect of surface Sb atomic fraction and non-metal dopants on CO_(2)RR activity,but also establishes a scalable ML prediction and screening framework,providing theoretical support and methodological pathways for the design of high-performance CuSb-based catalysts.展开更多
基金supported by the National Natural Science Foundation of China(No.No.52394202,No.52476056)State Key Laboratory of Engines,Tianjin University(K2025-10)+2 种基金the Natural Science Foundation of Chongqing(CSTB2024NSCQ-MSX0915)the China Postdoctoral Science Foundation(No.BX20240449)the Innovative Research Group Project of the National Natural Science Foundation of China(No.52021004).
文摘Electrochemical CO_(2) reduction(CO_(2)RR)to value-added fuels and chemicals offers a promising route toward carbon neutrality.However,developing efficient and selective catalysts for the generation of multi-carbon(C^(2+))products remains a significant challenge.In this work,we propose a combined density functional theory(DFT)and machine learning(ML)approach to systematically screen CuSb-based catalysts with varying surface Sb atomic fractions and non-metal dopants(O,N,S,Se,and P)on the Cu_(2)Sb(100)surface for CO_(2)RR.Approximately 200 representative adsorption configurations were randomly selected for DFT calculations,which were then used to train a predictive ML model.This model enables high-accuracy predictions of the adsorption energies of key intermediates(*CO and*H)for the remaining uncalculated configurations.By integrating the K-means clustering analysis and the optimal adsorption energy selection criteria based on the Sabatier principle,the candidate configuration with the best potential for C^(2+)product formation was identified:O-doped CuSb with a surface Sb atomic fraction of 3/12.Mechanistic studies further reveal that O doping significantly strengthens *CO adsorption while suppressing *H adsorption by modulating the electronic structure,thereby lowering the CO_(2)RR energy barrier and improving the thermodynamic selectivity toward C^(2+)products.This work not only elucidates the synergistic effect of surface Sb atomic fraction and non-metal dopants on CO_(2)RR activity,but also establishes a scalable ML prediction and screening framework,providing theoretical support and methodological pathways for the design of high-performance CuSb-based catalysts.
