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Equivariant Networks for Crystal Structures
Oumar Kaba · Siamak Ravanbakhsh

Tue Nov 29 09:00 AM -- 11:00 AM (PST) @ Hall J #431

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. We do this by defining a generalization of the message passing operations that can be used with more general permutation groups, or that can alternatively be seen as defining an expressive convolution operation on the crystal graph. Empirically, these models achieve competitive results with state-of-the-art on the Materials Project dataset.

Author Information

Oumar Kaba (Mila, McGill University)
Siamak Ravanbakhsh (McGill / MILA)

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