The models were trained using either SchNet or PaiNN representations, each with a radial cutoff of 10 atomic units (corresponding to 5.3 Å). The network architecture for learning the representation included 3 interaction layers for PaiNN-based models and 6 interaction layers for SchNet-based models. In all models, 256 features were employed to represent the atoms of a molecule, while three layers were utilized in each property network.