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Deep reaction network exploration at a heterogeneous catalytic interface
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 net...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9388529/ https://www.ncbi.nlm.nih.gov/pubmed/35982057 http://dx.doi.org/10.1038/s41467-022-32514-7 |
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author | Zhao, Qiyuan Xu, Yinan Greeley, Jeffrey Savoie, Brett M. |
author_facet | Zhao, Qiyuan Xu, Yinan Greeley, Jeffrey Savoie, Brett M. |
author_sort | Zhao, Qiyuan |
collection | PubMed |
description | 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 Ga(3+) 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. |
format | Online Article Text |
id | pubmed-9388529 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-93885292022-08-20 Deep reaction network exploration at a heterogeneous catalytic interface Zhao, Qiyuan Xu, Yinan Greeley, Jeffrey Savoie, Brett M. Nat Commun Article 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 Ga(3+) 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. Nature Publishing Group UK 2022-08-18 /pmc/articles/PMC9388529/ /pubmed/35982057 http://dx.doi.org/10.1038/s41467-022-32514-7 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Zhao, Qiyuan Xu, Yinan Greeley, Jeffrey Savoie, Brett M. Deep reaction network exploration at a heterogeneous catalytic interface |
title | Deep reaction network exploration at a heterogeneous catalytic interface |
title_full | Deep reaction network exploration at a heterogeneous catalytic interface |
title_fullStr | Deep reaction network exploration at a heterogeneous catalytic interface |
title_full_unstemmed | Deep reaction network exploration at a heterogeneous catalytic interface |
title_short | Deep reaction network exploration at a heterogeneous catalytic interface |
title_sort | deep reaction network exploration at a heterogeneous catalytic interface |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9388529/ https://www.ncbi.nlm.nih.gov/pubmed/35982057 http://dx.doi.org/10.1038/s41467-022-32514-7 |
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