Equivariant Networks for Crystal Structures

Understand

Supervised learning with deep models has tremendous potential for applications in materials science.

• Recently, graph neural networks have been used in this context, drawing direct inspiration from models for molecules.
• However, materials are typically much more structured than molecules, which is a feature that these models do not leverage.
• In this work, we introduce a class of models that are equivariant with respect to crystalline symmetry groups.