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Deep reaction network exploration at a heterogeneous catalytic interface.
Zhao, Qiyuan; Xu, Yinan; Greeley, Jeffrey; Savoie, Brett M.
Afiliación
  • Zhao Q; Davidson School of Chemical Engineering, Purdue University, West Lafayette, IN, 47906, USA.
  • Xu Y; Davidson School of Chemical Engineering, Purdue University, West Lafayette, IN, 47906, USA.
  • Greeley J; Davidson School of Chemical Engineering, Purdue University, West Lafayette, IN, 47906, USA. jgreeley@purdue.edu.
  • Savoie BM; Davidson School of Chemical Engineering, Purdue University, West Lafayette, IN, 47906, USA. bsavoie@purdue.edu.
Nat Commun ; 13(1): 4860, 2022 Aug 18.
Article en En | MEDLINE | ID: mdl-35982057
ABSTRACT
Characterizing the reaction energies and barriers of reaction networks is central to catalyst development. However, heterogeneous catalytic surfaces pose several unique challenges to automatic reaction network characterization, including large sizes and open-ended reactant sets, that make ad hoc network construction the current state-of-the-art. Here, we show how automated network exploration algorithms can be adapted to the constraints of heterogeneous systems using ethylene oligomerization on silica-supported single-site Ga3+ as a model system. Using only graph-based rules for exploring the network and elementary constraints based on activation energy and size for identifying network terminations, a comprehensive reaction network is generated and validated against standard methods. The algorithm (re)discovers the Ga-alkyl-centered Cossee-Arlman mechanism that is hypothesized to drive major product formation while also predicting several new pathways for producing alkanes and coke precursors. These results demonstrate that automated reaction exploration algorithms are rapidly maturing towards general purpose capability for exploratory catalytic applications.

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Nat Commun Asunto de la revista: BIOLOGIA / CIENCIA Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Nat Commun Asunto de la revista: BIOLOGIA / CIENCIA Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos
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