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.
Saved in:
Main Authors: | Zachary W. Ulissi, Andrew J. Medford, Thomas Bligaard, Jens K. Nørskov |
---|---|
Format: | article |
Language: | EN |
Published: |
Nature Portfolio
2017
|
Subjects: | |
Online Access: | https://doaj.org/article/f70daa3691ba4e61869251a1b60cfe88 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Understanding structural and molecular properties of complexes of nucleobases and Au13 golden nanocluster by DFT calculations and DFT-MD simulation
by: Ghazaleh Hashemkhani Shahnazari, et al.
Published: (2021) -
DFT STUDY ON THE MECHANISM OF THE ADDITION REACTION BETWEEN CARBENE AND GLYCINE
by: TAN,XIAOJUN, et al.
Published: (2011) -
NMR and DFT investigations of structure of colchicine in various solvents including density functional theory calculations
by: Gregory K. Pierens, et al.
Published: (2017) -
Electron, phonon and thermoelectric properties of Cu7PS6 crystal calculated at DFT level
by: B. Andriyevsky, et al.
Published: (2021) -
UV-VIS, NMR AND FT-IR SPECTRA OF TAUTOMERS OF VITAMIN C. EXPERIMENTAL AND DFT CALCULATIONS
by: DABBAGH,HUSSEIN A, et al.
Published: (2014)