Graph neural networks predict energetic and mechanical properties for models of solid solution metal alloy phases

We developed a PyTorch-based architecture called HydraGNN that implements graph convolutional neural networks (GCNNs) to predict the formation energy and the bulk modulus for models of solid solution alloys for various atomic crystal structures and relaxed volumes. We trained the GCNN surrogate model on a dataset for nickel-niobium (NiNb) generated by the embedded atom model (EAM) empirical interatomic potential for demonstration purposes. The dataset was generated by calculating the formation energy and the bulk modulus as a prototypical elastic property for optimized geometries starting from initial body-centered cubic (BCC), face-centered cubic (FCC), and hexagonal compact packed (HCP) crystal structures, with configurations spanning the possible compositional range for each of the three types of initial crystal structures. Numerical results show that the GCNN model effectively predicts both the formation energy and the bulk modulus as function of the optimized crystal structure, relaxed volume, and configurational entropy of the model structures for solid solution alloys.

Automated summary

We developed HydraGNN, a PyTorch-based architecture, which utilizes graph convolutional neural networks (GCNNs) to predict the formation energy and bulk modulus of solid solution alloy models with different atomic crystal structures and relaxed volumes. The GCNN surrogate model was trained using a dataset for nickel-niobium (NiNb) generated by the embedded atom model (EAM) empirical interatomic potential for demonstration purposes. The dataset was generated by calculating the formation energy and bulk modulus for optimized geometries starting from initial body-centered cubic (BCC), face-centered cubic (FCC), and hexagonal compact packed (HCP) crystal structures, covering the possible compositional range for each structure type. Numerical results demonstrate that the GCNN model effectively predicts the formation energy and bulk modulus based on the optimized crystal structure, relaxed volume, and configurational entropy of the solid solution alloy models.