  Materials discovery, especially for applications that require extreme
operating conditions, requires extensive testing that naturally limits the
ability to inquire the wealth of possible compositions. Machine Learning (ML)
has nowadays a well established role in facilitating this effort in systematic
ways. The increasing amount of available accurate DFT data represents a solid
basis upon which new ML models can be trained and tested. While conventional
models rely on static descriptors, generally suitable for a limited class of
systems, the flexibility of Graph Neural Networks (GNNs) allows for direct
learning representations on graphs, such as the ones formed by crystals. We
utilize crystal graph neural networks (CGNN) to predict crystal properties with
DFT level accuracy, through graphs with encoding of the atomic (node/vertex),
bond (edge), and global state attributes. In this work, we aim at testing the
ability of the CGNN MegNet framework in predicting a number of properties of
systems previously unseen from the model, obtained by adding a substitutional
defect in bulk crystals that are included in the training set. We perform DFT
validation to assess the accuracy in the prediction of formation energies and
structural features (such as elastic moduli). Using CGNNs, one may identify
promising paths in alloy discovery.
