  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
property prediction tasks.
