Junfu Tan

SO(3)-equivariant graph-neural-network interatomic potentials usually use one cutoff radius, so radial interactions carry a single characteristic spatial-frequency scale. MCSA fuses radial kernels with different cutoff radii before the angular tensor product, covering several scales with one angular computation. Across the Nequix, Eqnorm and EquiformerV3 backbones, energy, force and stress errors fall on both in-distribution and out-of-distribution tests; Nequix+MCSA lowers the symmetric relative mean error of lattice thermal conductivity from 0.487 to 0.420, with per-material time rising from 1.107 to 1.136 minutes.

SO(3)-equivariant graph-neural-network interatomic potentials usually use one cutoff radius, so radial interactions carry a single characteristic spatial-frequency scale. MCSA fuses radial kernels with different cutoff radii before the angular tensor product, covering several scales with one angular computation. Across the Nequix, Eqnorm and EquiformerV3 backbones, energy, force and stress errors fall on both in-distribution and out-of-distribution tests; Nequix+MCSA lowers the symmetric relative mean error of lattice thermal conductivity from 0.487 to 0.420, with per-material time rising from 1.107 to 1.136 minutes.