NoRRA: Nonlocal Representation and Response Augmentation for Machine Learning Interatomic Models

Machine-learning interatomic potentials (MLIPs) achieve efficient atomistic simulation by constructing each atomic representation from neighbors within a finite cutoff, but this locality can miss physical responses that depend on structure beyond the cutoff. Two complementary strategies address this limitation: nonlocal representation, which supplies local MLIPs with global structural context, and nonlocal energy response, which constructs an explicit nonlocal interaction energy from latent variables such as charges or dipoles. We introduce NoRRA (Nonlocal Representation and Response Augmentation), a plug-in framework that couples both pathways in a local MLIP. Reciprocal-space features inject global periodic context before a replaceable backbone, while a separate nonlocal-response readout predicts constrained latent charges and evaluates an explicit interaction energy through a learnable sum-of-Gaussians kernel. On the same Point Edge Transformer (PET) backbone, NoRRA adds only $0.387\%$ parameters and reduces SPICE relative-energy MAE from the baseline value of $19.798$ to $14.297$ kJ/mol. This framework also transfers to MatterSim. Across molecular, ionic, interfacial, and electrochemical systems, NoRRA improves energy and force accuracy and downstream physical properties, including molten-salt thermal expansion, the liquid structure of NaCl, and the long-range force tails of ion pairs. Component ablations show that the two pathways are complementary, with the largest gains in ionic regimes where Coulomb interactions dominate.