Another limitation of the input data set is the use of MD structures without force field parameters for the radicals. We correct these structures implicitly in the GNN by training with barriers obtained from DFT optimization in the transfer learning step. While DFT would still be more accurate than MD alone, having proper parameters for the radical available would lower the complexity of the task the model is facing, since the input structures would be more similar to the DFT structures. We would expect an acceptable accuracy even when omitting the transfer learning step, A B Fig. Differently trained models evaluated on comparable subsets of the test data. (A) Comparison between models trained on different data sets as indicated on the y-axis. 2A, 3A, and aA correspond to data with translations below 2A, 3A, or all translations, respectively. 'traj' refers to data only from trajectories, 'both' includes in addition the synthetic data. Four models were trained per data set. (B) Comparison between transfer learning and training directly on the optimized data only. Again, both models were evaluated on trajectory data < 3 Å and < 2 Å. All ten original models are used in transfer learning, ten new models were trained for the direct learning approach.