Quantitative estimates of reaction
barriers are essential for developing
kinetic mechanisms and predicting reaction outcomes. However, the
lack of experimental data and the steep scaling of accurate quantum
calculations often hinder the ability to obtain reliable kinetic values.
Here, we train a directed message passing neural network on nearly
24,000 diverse gas-phase reactions calculated at CCSD(T)-F12a/cc-pVDZ-F12//ωB97X-D3/def2-TZVP.
Our model uses 75% fewer parameters than previous studies, an improved
reaction representation, and proper data splits to accurately estimate
performance on unseen reactions. Using information from only the reactant
and product, our model quickly predicts barrier heights with a testing
MAE of 2.6 kcal mol–1 relative to the coupled-cluster
data, making it more accurate than a good density functional theory
calculation. Furthermore, our results show that future modeling efforts
to estimate reaction properties would significantly benefit from fine-tuning
calibration using a transfer learning technique. We anticipate this
model will accelerate and improve kinetic predictions for small molecule
chemistry.