  Different machine learning (ML) models are proposed in the present work to
predict DFT-quality barrier heights (BHs) from semiempirical quantum-mechanical
(SQM) calculations. The ML models include multi-task deep neural network,
gradient boosted trees by means of the XGBoost interface, and Gaussian process
regression. The obtained mean absolute errors (MAEs) are similar or slightly
better than previous models considering the same number of data points. Unlike
other ML models employed to predict BHs, entropic effects are included, which
enables the prediction of rate constants at different temperatures. The ML
corrections proposed in this paper could be useful for rapid screening of the
large reaction networks that appear in Combustion Chemistry or in
Astrochemistry. Finally, our results show that 70% of the bespoke predictors
are amongst the features with the highest impact on model output. This
custom-made set of predictors could be employed by future delta-ML models to
improve the quantitative prediction of other reaction properties.
