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

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

Automated summary

We introduce the Crystal Edge Graph Attention Neural Network (CEGANN) workflow, which utilizes a graph attention-based architecture to learn unique feature representations and perform material classification across multiple scales and diverse classes. CEGANN can classify based on global structure-level representations and is demonstrated to be transferable for local atom-level classification tasks. It is capable of classifying in noisy dynamical environments and can handle multicomponent systems with thermal noise and compositional diversity. Our approach allows for material agnostic multiscale feature classification.