Exploring catalytic reaction networks with machine learning
Author:
Funder
Alexander von Humboldt-Stiftung
Publisher
Springer Science and Business Media LLC
Subject
Process Chemistry and Technology,Biochemistry,Bioengineering,Catalysis
Link
https://www.nature.com/articles/s41929-022-00896-y.pdf
Reference85 articles.
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2. Ulissi, Z. W., Medford, A. J., Bligaard, T. & Nørskov, J. K. To address surface reaction network complexity using scaling relations machine learning and DFT calculations. Nat. Commun. 8, 14621 (2017).
3. Stocker, S., Csányi, G., Reuter, K. & Margraf, J. T. Machine learning in chemical reaction space. Nat. Commun. 11, 5505 (2020).
4. Stamatakis, M. Kinetic modelling of heterogeneous catalytic systems. J. Phys. Condens. Matter 27, 013001 (2015).
5. Sutton, J. E. & Vlachos, D. G. Building large microkinetic models with first-principles’ accuracy at reduced computational cost. Chem. Eng. Sci. 121, 190–199 (2015).
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