An alternate strategy is to train models to approximate subdivision invariance via data augmentation. Using data augmentation to train approximate invariances in different DL contexts (e.g. image rotation, reflection, etc. in CNNs, molecular rotation and translation in interatomic potentials ) is well established and can enhance model prediction accuracy and robustness. We apply this augmentation strategy to our hetero-GNN model by artificially subdividing the UCNP input with the same labels (emission intensities) but with structural representations modified with random subdivisions. This data augmentation is meant to guide the learned latent representation to exhibit approximate subdivision invariance, which should improve model performance. This strategy is implemented on-the-fly (Fig. ), so that data in each batch are augmented as they appear during training, rather than augmenting the entire dataset before training. Thus, this does not explicitly increase the size of the training dataset, but implicitly multiplies the number of unique heterograph representations of UCNPs seen by the model during training by the number of training epochs. Random subdivisions result in a node with r inner , r outer , being split into two nodes with r inner , r subdivision and r subdivision , r outer , where r inner < r subdivision < r outer . Additional details about data augmentation are provided in Section 5.