Scaling Laws and Symmetry, Evidence from Neural Force Fields

ArXi:2510.09768v2 Announce Type: replace We present an empirical study in the geometric task of learning interatomic potentials, which shows equivariance matters even at larger scales; we show a clear power-law scaling behaviour with respect to data, parameters and compute with ``architecture-dependent exponents''. In particular, we observe that equivariant architectures, which leverage task symmetry, scale better than non-equivariant models. Moreover, among equivariant architectures, higher-order representations translate to better scaling exponents.