To correct for the deviation between the barriers computed for non-optimized and optimized reaction paths, models already trained on non-optimized systems were retrained in a transfer learning scheme to be as data-efficient as possible. When using transfer learning, one often freezes most of the network and retrains only parts of it. Here, however, we found the best results when not freezing any part of the model. Still, models trained with transfer learning substantially outperform models trained on optimized data alone. (Figure ) Note that only the training target, i.e., the barrier, was changed for transfer learning, and not the input to the model. The same non-optimized structures are fed into the model, as it is intended to be used on non-optimized structures from MD simulations. In other words, the model learns a mapping between non-optimized MD structures and DFT barriers of the optimized reaction paths. The ensemble model for predicting optimized barriers achieves an MAE of 3.6 ± 3.2 kcal/mol on trajectory data with translations of less than 2 Å and 4.9 ± 4.0 kcal/mol on translations less than 3 Å(Figure A and B, respectively). The learning curve of the transfer learning procedure in Figure C suggests that the model is data limited, as the accuracy increases in particular from 90% over 95% to 100% of the optimized test data. Training curves are provided in Figure .