To address surface reaction network complexity using scaling relations machine learning and DFT calculations

Finding catalyst mechanisms remains a challenge due to the complexity of hydrocarbon chemistry. Here, the authors shows that scaling relations and machine-learning methods can focus full-accuracy methods on the small subset of rate-limiting reactions allowing larger reaction networks to be treated.

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Detalles Bibliográficos
Autores principales:	Zachary W. Ulissi, Andrew J. Medford, Thomas Bligaard, Jens K. Nørskov
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/f70daa3691ba4e61869251a1b60cfe88
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spelling	oai:doaj.org-article:f70daa3691ba4e61869251a1b60cfe882021-12-02T15:38:53ZTo address surface reaction network complexity using scaling relations machine learning and DFT calculations10.1038/ncomms146212041-1723https://doaj.org/article/f70daa3691ba4e61869251a1b60cfe882017-03-01T00:00:00Zhttps://doi.org/10.1038/ncomms14621https://doaj.org/toc/2041-1723Finding catalyst mechanisms remains a challenge due to the complexity of hydrocarbon chemistry. Here, the authors shows that scaling relations and machine-learning methods can focus full-accuracy methods on the small subset of rate-limiting reactions allowing larger reaction networks to be treated.Zachary W. UlissiAndrew J. MedfordThomas BligaardJens K. NørskovNature PortfolioarticleScienceQENNature Communications, Vol 8, Iss 1, Pp 1-7 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Zachary W. Ulissi Andrew J. Medford Thomas Bligaard Jens K. Nørskov To address surface reaction network complexity using scaling relations machine learning and DFT calculations
description	Finding catalyst mechanisms remains a challenge due to the complexity of hydrocarbon chemistry. Here, the authors shows that scaling relations and machine-learning methods can focus full-accuracy methods on the small subset of rate-limiting reactions allowing larger reaction networks to be treated.
format	article
author	Zachary W. Ulissi Andrew J. Medford Thomas Bligaard Jens K. Nørskov
author_facet	Zachary W. Ulissi Andrew J. Medford Thomas Bligaard Jens K. Nørskov
author_sort	Zachary W. Ulissi
title	To address surface reaction network complexity using scaling relations machine learning and DFT calculations
title_short	To address surface reaction network complexity using scaling relations machine learning and DFT calculations
title_full	To address surface reaction network complexity using scaling relations machine learning and DFT calculations
title_fullStr	To address surface reaction network complexity using scaling relations machine learning and DFT calculations
title_full_unstemmed	To address surface reaction network complexity using scaling relations machine learning and DFT calculations
title_sort	to address surface reaction network complexity using scaling relations machine learning and dft calculations
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/f70daa3691ba4e61869251a1b60cfe88
work_keys_str_mv	AT zacharywulissi toaddresssurfacereactionnetworkcomplexityusingscalingrelationsmachinelearninganddftcalculations AT andrewjmedford toaddresssurfacereactionnetworkcomplexityusingscalingrelationsmachinelearninganddftcalculations AT thomasbligaard toaddresssurfacereactionnetworkcomplexityusingscalingrelationsmachinelearninganddftcalculations AT jensknørskov toaddresssurfacereactionnetworkcomplexityusingscalingrelationsmachinelearninganddftcalculations
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To address surface reaction network complexity using scaling relations machine learning and DFT calculations

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