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Micro-kinetic modelling of the CO reduction reaction on single atom catalysts accelerated by machine learning.
Zhang, Qing-Meng; Wang, Zhao-Yu; Zhang, Hao; Liu, Xiao-Hong; Zhang, Wei; Zhao, Liu-Bin.
Afiliación
  • Zhang QM; Department of Chemistry, School of Chemistry and Chemical Engineering, Southwest University, Chongqing, 400715, China. haozhang@swu.edu.cn.
  • Wang ZY; Department of Chemistry, School of Chemistry and Chemical Engineering, Southwest University, Chongqing, 400715, China. haozhang@swu.edu.cn.
  • Zhang H; Department of Chemistry, School of Chemistry and Chemical Engineering, Southwest University, Chongqing, 400715, China. haozhang@swu.edu.cn.
  • Liu XH; Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, 400714, China.
  • Zhang W; National University of Singapore (Chongqing) Research Institute, Chongqing 401123, China. xiaohong.liu@nusricq.cn.
  • Zhao LB; Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, 400714, China.
Phys Chem Chem Phys ; 26(14): 11037-11047, 2024 Apr 03.
Article en En | MEDLINE | ID: mdl-38526740
ABSTRACT
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.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Phys Chem Chem Phys Asunto de la revista: BIOFISICA / QUIMICA Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Reino Unido
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Phys Chem Chem Phys Asunto de la revista: BIOFISICA / QUIMICA Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Reino Unido