CEGANN: Crystal Edge Graph Attention Neural Network for multiscale classification of materials environment

Abstract We introduce Crystal Edge Graph Attention Neural Network (CEGANN) workflow that uses graph attention-based architecture to learn unique feature representations and perform classification of materials across multiple scales (from atomic mesoscale) diverse classes ranging from metals, oxides, non-metals hierarchical such as zeolites semi-ordered mesophases. CEGANN can classify based on a global, structure-level representation space group dimensionality (e.g., bulk, 2D, clusters, etc.). Using representative polycrystals zeolites, we demonstrate its transferability in performing local atom-level tasks, grain boundary identification other heterointerfaces. classifies (thermal) noisy dynamical environments demonstrated for zeolite nucleation growth an amorphous mixture. Finally, use multicomponent systems with thermal noise compositional diversity. Overall, our approach is material agnostic allows multiscale atomic-scale crystals heterointerfaces microscale boundaries.