Data-driven modeling techniques allow for inexpensive and accurate predictions of material properties and thereby enable rapid screenings of large material search spaces to select potential material candidates with desirable properties . In particular, deep learning (DL) models have the potential to accurately represent complex relations between input features and target quantities, but require large volumes of training data to attain high accuracy. Graph convolutional neural networks (GCNNs) are a particular type of DL models, well suited to process data represented as graph samples. Currently, GCNN models are used as surrogates in material science to predict material properties from atomic information by directly mapping the atomic structure input to graphs, with atoms as graph nodes and chemical bonds, or nearest-neighbor interactions, as edges .