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Machine Learning Reactivity in the Chemical Space Surrounding Vaska's Complex

preprint
submitted on 19.11.2019, 16:39 and posted on 27.11.2019, 17:49 by Pascal Friederich, Gabriel dos Passos Gomes, Riccardo De Bin, Alan Aspuru-Guzik, David Balcells
Machine learning models, including neural networks, Bayesian optimization, gradient boosting and Gaussian processes, were trained with DFT data for the accurate, affordable and explainable prediction of hydrogen activation barriers in the chemical space surrounding Vaska's complex.

History

Email Address of Submitting Author

david.balcells@kjemi.uio.no

Institution

University of Oslo

Country

Norway

ORCID For Submitting Author

0000-0002-3389-0543

Declaration of Conflict of Interest

No conflict of interest

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