  The presence of point defects such as vacancies plays an important role in
material design. Here, we demonstrate that a graph neural network (GNN) model
trained only on perfect materials can also be used to predict vacancy formation
energies ($E_{vac}$) of defect structures without the need for additional
training data. Such GNN-based predictions are considerably faster than density
functional theory (DFT) calculations with reasonable accuracy and show the
potential that GNNs are able to capture a functional form for energy
predictions. To test this strategy, we developed a DFT dataset of 508 $E_{vac}$
consisting of 3D elemental solids, alloys, oxides, nitrides, and 2D monolayer
materials. We analyzed and discussed the applicability of such direct and fast
predictions. We applied the model to predict 192494 $E_{vac}$ for 55723
materials in the JARVIS-DFT database.
