Improving Accuracy and Transferability of Machine Learning Chemical Activation Energies by Adding Electronic Structure Information

Predicting chemical activation energies is one of the
longstanding
and important challenges in computational chemistry. Recent advances
have shown that machine learning can be used to create tools to predict
them. Such tools can significantly decrease the computational cost
for these predictions compared to traditional methods, which require
an optimal path search along a high-dimensional potential energy surface.
To enable this new route, we need both large and accurate datasets
and a compact yet complete description of the reactions. Although
data for chemical reactions is becoming increasingly available, the
key step of encoding the reaction as an efficient descriptor remains
a big challenge. In this paper, we demonstrate that including electronic
energy levels in the description of the reaction significantly improves
the prediction accuracy and transferability. Feature importance analysis
further demonstrates that electronic energy levels have a higher importance
than some structural information and typically require less space
in the reaction encoding vector. In general, we observe that the results
of the feature importance analysis relate well to the domain knowledge
of fundamental chemical principles. This work can help to build better
chemical reaction encodings for machine learning and thus improve
the predictions of machine learning models for reaction activation
energies. These models could ultimately be used to recognize reaction
limiting steps in large reaction systems, allowing to account for
bottlenecks at the design stage.