  There has been a recent surge of interest in using machine learning to
approximate density functional theory (DFT) in materials science. However, many
of the most performant models are evaluated on large databases of computed
properties of, primarily, materials with precise atomic coordinates available,
and which have been experimentally synthesized, i.e., which are
thermodynamically stable or metastable. These aspects provide challenges when
applying such models on theoretical candidate materials, for example for
materials discovery, where the coordinates are not known. To extend the scope
of this methodology, we investigate the performance of the Crystal Graph
Convolutional Neural Network (CGCNN) on a data set of theoretical structures in
three related ternary phase diagrams (Ti,Zr,Hf)-Zn-N, which thus include many
highly unstable structures. We then investigate the impact on the performance
of using atomic positions that are only partially relaxed into local energy
minima. We also explore options for improving the performance in these
scenarios by transfer learning, either from models trained on a large database
of mostly stable systems, or a different but related phase diagram. Models
pre-trained on stable materials do not significantly improve performance, but
models trained on similar data transfer very well. We demonstrate how our
findings can be utilized to generate phase diagrams with a major reduction in
computational effort.
