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	Micro-kinetic modelling of the CO reduction reaction on single atom catalysts accelerated by machine learning
	Zhang, Qing-Meng1; Wang, Zhao-Yu1; Zhang, Hao1; Liu, Xiao-Hong2,3; Zhang, Wei2; Zhao, Liu-Bin1
	2024-04-03
摘要	Electrochemical CO2 transformation to fuels and chemicals is an effective strategy for conversion of renewable electric energy into storable chemical energy in combination with reducing green-house gas emission. Metal-nitrogen-carbon (M-N-C) single atom catalysts (SAC) have shown great potential in the electrochemical CO2 reduction reaction (CO2RR). However, exploring advanced SACs with simultaneously high catalytic activity and high product selectivity remains a great challenge. In this study, density functional theory (DFT) calculations are combined with machine learning (ML) for rapid and high-throughput screening of high performance CO reduction catalysts. Firstly, the electrochemical properties of 99 M-N-C SACs were calculated by DFT and used as a database. By using different machine learning models with simple features, the investigated SACs were expanded from 99 to 297. Through several effective indicators of catalyst stability, inhibition of the hydrogen evolution reaction, and CO adsorption strength, 33 SACs were finally selected. The catalytic activity and selectivity of the remaining 33 SACs were explored by micro-kinetic simulation based on Marcus theory. Among all the studied SACs, Mn-NC2, Pt-NC2, and Au-NC2 deliver the best catalytic performance and can be used as potential catalysts for CO2/CO conversion to hydrocarbons with high energy density. This effective screening method using a machine learning algorithm can promote the exploration of CO2RR catalysts and significantly reduce the simulation cost. Density functional theory calculations are combined with machine learning for rapid and high-throughput screening of high performance single-atom catalysts for the CO reduction reaction.
DOI	10.1039/d4cp00325j
发表期刊	PHYSICAL CHEMISTRY CHEMICAL PHYSICS
ISSN	1463-9076
卷号	26期号:14页码:11037-11047
通讯作者	Zhang, Hao(haozhang@swu.edu.cn) ; Liu, Xiao-Hong(xiaohong.liu@nusricq.cn) ; Zhao, Liu-Bin(lbzhao@swu.edu.cn)
收录类别	SCI
WOS记录号	WOS:001190250300001
语种	英语