For the QM9 dataset, which features chemically diverse molecules, we modified the final layer of an E(n)-equivariant graph neural network with a deep evidential regression layer (EGNN-DER) to estimate the uncertainty in predicting electronic spatial extent. Given the lower computational cost of a single model, this approach was well-suited for scalar property predictions. The uncertainty quantification for QM9 was performed within the redistributed data, where the dataset was split into 80:10:10 for training, validation, and testing.