  3D Euclidean symmetry equivariant neural networks have demonstrated notable
success in modeling complex physical systems. We introduce a framework for
relaxed $E(3)$ graph equivariant neural networks that can learn and represent
symmetry breaking within continuous groups. Building on the existing e3nn
framework, we propose the use of relaxed weights to allow for controlled
symmetry breaking. We show empirically that these relaxed weights learn the
correct amount of symmetry breaking.
