To understand to what degree a given part of the data improves the model, multiple models were trained on different parts of the training data and evaluated on a subset of the test data. The models were trained on trajectory and synthetic data, or on trajectory data only. Additionally, several translation cutoffs were used, either at 2 or 3 Å or without a cutoff. In all cases, the models were evaluated on trajectory data below 2 or 3 Å. This setup shows that the model benefits from being trained on synthetic systems alongside trajectory systems, even if it is evaluated only on trajectory systems. (Figure ) Similarly, the model performance for systems with translations smaller than 2 Å improves when it is trained on larger translations. Data that is rather distant from the target prediction with regard to its chemistry and geometry still adds to the predictive power of the model. Therefore, we trained the final model on synthetic and trajectory data without a translation cutoff.