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Machine Learning Big Data Set Analysis Reveals C-C Electro-Coupling Mechanism.
Li, Haobo; Li, Xinyu; Wang, Pengtang; Zhang, Zhen; Davey, Kenneth; Shi, Javen Qinfeng; Qiao, Shi-Zhang.
Afiliación
  • Li H; School of Chemical Engineering, the University of Adelaide, Adelaide SA 5005, Australia.
  • Li X; Australian Institute for Machine Learning, the University of Adelaide, Adelaide SA 5000, Australia.
  • Wang P; School of Chemical Engineering, the University of Adelaide, Adelaide SA 5005, Australia.
  • Zhang Z; Australian Institute for Machine Learning, the University of Adelaide, Adelaide SA 5000, Australia.
  • Davey K; School of Chemical Engineering, the University of Adelaide, Adelaide SA 5005, Australia.
  • Shi JQ; Australian Institute for Machine Learning, the University of Adelaide, Adelaide SA 5000, Australia.
  • Qiao SZ; School of Chemical Engineering, the University of Adelaide, Adelaide SA 5005, Australia.
J Am Chem Soc ; 146(32): 22850-22858, 2024 Aug 14.
Article en En | MEDLINE | ID: mdl-39096280
ABSTRACT
Carbon-carbon (C-C) coupling is essential in the electrocatalytic reduction of CO2 for the production of green chemicals. However, due to the complexity of the reaction network, there remains controversy regarding the underlying reaction mechanisms and the optimal direction for catalyst material design. Here, we present a global perspective to establish a comprehensive data set encompassing all C-C coupling precursors and catalytic active site compositions to explore the reaction mechanisms and screen catalysts via big data set analysis. The 2D-3D ensemble machine learning strategy, developed to target a variety of adsorption configurations, can quickly and accurately expand quantum chemical calculation data, enabling the rapid acquisition of this extensive big data set. Analyses of the big data set establish that (1) asymmetric coupling mechanisms exhibit greater potential efficiency compared to symmetric coupling, with the optimal path involving the coupling CHO with CH or CH2, and (2) C-C coupling selectivity of Cu-based catalysts can be enhanced through bimetallic doping including CuAgNb sites. Importantly, we experimentally substantiate the CuAgNb catalyst to demonstrate actual boosted performance in C-C coupling. Our finding evidence the practicality of our big data set generated from machine learning-accelerated quantum chemical computations. We conclude that combining big data with complex catalytic reaction mechanisms and catalyst compositions will set a new paradigm for accelerating optimal catalyst design.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: J Am Chem Soc Año: 2024 Tipo del documento: Article País de afiliación: Australia Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: J Am Chem Soc Año: 2024 Tipo del documento: Article País de afiliación: Australia Pais de publicación: Estados Unidos