A Hybrid Equivariant–Scalar Message Passing Framework for Geometric Molecular Graphs

Geometric graph neural networks are widely applied to molecular systems, typically relying on local neighborhoods and a common message passing scheme across all distances. However, the distinct physical nature of short-range many-body effects and long-range behavior suggests using separate, distance-dependent representations. We propose a hybrid message passing framework combining equivariant local updates with scalar longer-range interactions. We evaluate the approach on molecular energy and force prediction tasks, analyzing the trade-off between accuracy and computational cost across cutoff configurations. Our results show that combining equivariant and scalar updates can achieve accuracy comparable to fully equivariant models while substantially reducing computational cost.