Deep Learning of Activation Energies

Quantitative
predictions of reaction properties, such as activation
energy, have been limited due to a lack of available training data.
Such predictions would be useful for computer-assisted reaction mechanism
generation and organic synthesis planning. We develop a template-free
deep learning model to predict the activation energy given reactant
and product graphs and train the model on a new, diverse data set
of gas-phase quantum chemistry reactions. We demonstrate that our
model achieves accurate predictions and agrees with an intuitive understanding
of chemical reactivity. With the continued generation of quantitative
chemical reaction data and the development of methods that leverage
such data, we expect many more methods for reactivity prediction to
become available in the near future.