COMPUTATIONAL RESEARCH in BOSTON and BEYOND (CRIBB)

We demonstrate how E(3)-Equivariant Neural Networks (ENNs) can detect symmetry breaking in physical data and reveal hidden symmetry-implied information. Symmetry breaking, whether spontaneous (as in crystalline phase transitions) or induced by external forces, plays a crucial role in our understanding of physical systems. We show that an external input parameter to a fully equivariant model can be used to break symmetry in a physically interpretable way. We apply our approach to altermagnets, a new class of magnetic materials, in order to learn magnetic multipoles.