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

2019 · doi:10.26434/chemrxiv.10347566.v1
preprint OA: closed CC-BY-NC-ND-4.0
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Abstract

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.

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License: CC-BY-NC-ND-4.0