TPE performs equally well for GNNs. We repeat the TPE procedure, varying the learning rate and batch size, for SchNet, PaiNN, and SpookyNet, training on 10,000 frames (1,000 frames/molecule) from the revised MD-17 dataset of 10 small organic molecules. Using only 20% of the total training budget, we achieve excellent predictive power (Figure ) with TPE for SchNet and PaiNN. The variance in model performance using the entire training budget is significant, indicating the importance of proper HPO.