Quantum-mechanical transition-state model combined with machine learning provides catalyst design features for selective Cr olefin oligomerization

The use of data science tools to provide the emergence of non-trivial chemical features for catalyst design is an important goal in catalysis science. Additionally, there is currently no general strategy for computational homogeneous, molecular catalyst design. Here, we report the unique combination...

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Detalles Bibliográficos
Autores principales: Maley, Steven M., Kwon, Doo-Hyun, Rollins, Nick, Stanley, Johnathan C., Sydora, Orson L., Bischof, Steven M., Ess, Daniel H.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Royal Society of Chemistry 2020
Materias:
Chemistry
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8161675/
https://www.ncbi.nlm.nih.gov/pubmed/34094231
http://dx.doi.org/10.1039/d0sc03552a

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8161675/
https://www.ncbi.nlm.nih.gov/pubmed/34094231
http://dx.doi.org/10.1039/d0sc03552a

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