During training, we use the standard features provided by Chemprop. For each property, the training and validation data were striped into nine folds with a ratio of 8:1 training to validation. We optimized the model's hyperparameters on one split of data, using 50 iterations across the default search space using the Ray Tune package. The optimal hyperparameters for each property were selected based on the lowest validation error. A model was trained for each fold over 50 epochs, resulting in an ensemble of 9 models. Each model was trained with random initializations of weights and the hyperparameters obtained previously.