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

27 November 2019, Version 1
This content is a preprint and has not undergone peer review at the time of posting.

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.

Keywords

dft
machine learning
chemical space
reactivity
Neural Networks
Bayesian optimization
gradient boosting
gaussian processes
autocorrelation functions
fingerprints
predictions
hydrogen activation
catalysis
transition metal complexes

Supplementary materials

Title
Description
Actions
Title
Learning Barriers SI ChRx
Description
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Title
Vaskas Space Data
Description
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