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

Surface reaction networks involving hydrocarbons exhibit enormous complexity with thousands of species and reactions for all but the very simplest of chemistries. We present a framework for optimization under uncertainty for heterogeneous catalysis reaction networks using surrogate models that are t...

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Detalles Bibliográficos
Autores principales: Ulissi, Zachary W., Medford, Andrew J., Bligaard, Thomas, Nørskov, Jens K.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group 2017
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5343483/
https://www.ncbi.nlm.nih.gov/pubmed/28262694
http://dx.doi.org/10.1038/ncomms14621

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5343483/
https://www.ncbi.nlm.nih.gov/pubmed/28262694
http://dx.doi.org/10.1038/ncomms14621

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