  Applications of machine learning techniques for materials modeling typically
involve functions known to be equivariant or invariant to specific symmetries.
While graph neural networks (GNNs) have proven successful in such tasks, they
enforce symmetries via the model architecture, which often reduces their
expressivity, scalability and comprehensibility. In this paper, we introduce
(1) a flexible framework relying on stochastic frame-averaging (SFA) to make
any model E(3)-equivariant or invariant through data transformations. (2)
FAENet: a simple, fast and expressive GNN, optimized for SFA, that processes
geometric information without any symmetrypreserving design constraints. We
prove the validity of our method theoretically and empirically demonstrate its
superior accuracy and computational scalability in materials modeling on the
OC20 dataset (S2EF, IS2RE) as well as common molecular modeling tasks (QM9,
QM7-X). A package implementation is available at https://faenet.readthedocs.io.
