Rethinking Edge-wise Computation for Efficient Equivariant Interatomic Potentials

Modern equivariant machine learning interatomic potentials are becoming increasingly computationally demanding as their architectures grow more sophisticated. In these architectures, edge-wise operations often account for a substantial portion of the overall computational cost. However, increasing edge-wise complexity is not necessarily the most efficient way to improve model accuracy. Keeping edge-wise computation simple enables effective use of modern fused kernels, substantially improving computational throughput. We therefore introduce EquFlash-S, an equivariant architecture that combines lightweight edge-wise message passing with expressive nonlinear processing in a separate residual node-wise feed-forward block. Its low per-edge cost enables scaling to larger model capacities for higher accuracy while retaining substantially higher throughput than more complex edge-wise designs. EquFlash-S achieves competitive performance across diverse atomistic benchmarks, including Matbench Discovery, the MDR Phonon Benchmark, and OC20. In particular, on Matbench Discovery, EquFlash-S achieves a Combined Performance Score of 0.907 while delivering 2.7–5.7$\times$ higher throughput than state-of-the-art models with comparable accuracy.