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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Auteurs principaux: | Zachary W. Ulissi, Andrew J. Medford, Thomas Bligaard, Jens K. Nørskov |
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Format: | article |
Langue: | EN |
Publié: |
Nature Portfolio
2017
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Sujets: | |
Accès en ligne: | https://doaj.org/article/f70daa3691ba4e61869251a1b60cfe88 |
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