Abstract
There has been a recent surge of interest in using machine learning to approximate density functional theory 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 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 pretrained 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.
Highlights
Discovering new materials is a driving force for new technologies
A concurrent study by Pandey et al [30] shows that models trained on Inorganic Crystal Structure Database (ICSD) data perform poorly when applied to unstable materials, and we aim to investigate further how to cope with this issue
We have demonstrated that crystal graph convolutional neural network (CGCNN) can be trained to predict formation energies of materials that belong to a single ternary phase diagram and which for the most part are far from the convex hull
Summary
Discovering new materials is a driving force for new technologies. With the increase in computational resources, there has been a surge in available data from automated high-throughput materials simulations, obtained using supercomputers. A central concern in the design of a new material is its thermodynamical stability This is determined by the formation energy of a phase in relation to that of other phases at the same composition, as well as the most stable combination of competing phases it can decompose into. A phase is stable if its formation energy is lower than any linear combination of competing phases This means that the stability of each phase can be determined by constructing a convex hull of the phases in the phase diagram [38,39]. To generate a phase diagram without any prior knowledge of which phases are stable, a large number of potential materials needs to be considered since one in principle needs to determine the formation energy of every possible phase at every composition
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