Cargando…
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...
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: | |
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 |
Ejemplares similares
-
Calibrating DFT Formation
Enthalpy Calculations by
Multifidelity Machine Learning
por: Gong, Sheng, et al.
Publicado: (2022) -
Proton transfers in the Strecker reaction revealed by DFT calculations
por: Yamabe, Shinichi, et al.
Publicado: (2014) -
High-throughput calculations of catalytic properties of bimetallic alloy surfaces
por: Mamun, Osman, et al.
Publicado: (2019) -
Modelling Catalyst Surfaces Using DFT Cluster Calculations
por: Czekaj, Izabela, et al.
Publicado: (2009) -
Fundamental concepts in heterogeneous catalysis
por: Norskov, Jens K, et al.
Publicado: (2014)