Accurate interatomic potentials enable molecular dynamics of materials, molecules, and interfaces beyond density-functional-theory length and time scales. Equivariant neural network potentials have improved the representation of local geometry. However, their deployable energy surfaces ultimately manifest through invariant scalar channels, whose aggregation and spectral resolution remain comparatively underexamined. Here we use Physics-Aware Neighborhood (PAN) pooling and Physics-Guided Spectral (PGS) mixers as controlled scalar-pathway probes: lightweight, symmetry-preserving modifications that act only on \(\ell=0\) channels while leaving the equivariant tensor backbone unchanged. Using MACE as a high-body-order mechanistic scaffold, PAN adds coordination-sensitive amplitude modulation, whereas PGS augments edge and readout scalar features with radial and tapered spectral bases. Across metallic Ag, covalent Si, a short-range ionic LiF/Li--F subset, and MD17/rMD17 molecules, this scalar-pathway correction reduces MACE force errors by 22--27\% and energy errors by 19--22\%; on systems with stress labels, stress errors decrease by 27--28\%, at approximately 5\% additional inference-FLOPs cost. Directionally consistent gains in Allegro and NequIP further indicate that the correction is portable across distinct short-range equivariant backbones, although effect sizes remain architecture-dependent. These results identify scalar-pathway fidelity as a practical design dimension for short-range equivariant interatomic potentials.

翻译：精确的原子间势能使得材料和分子以及界面的分子动力学模拟能够超越密度泛函理论的时间和尺度限制。等变神经网络势能已经改善了局部几何结构表示。然而，其可部署的能量表面最终通过不变标量通道体现，这些通道的聚合和谱分辨率仍相对缺乏研究。本文使用物理感知邻域（PAN）池化和物理引导谱（PGS）混合器作为受控标量路径探针：这些轻量级、保持对称性的修改仅作用于\(\ell=0\)通道，同时保留等变张量主干不变。以MACE作为高体序机制框架，PAN添加了配位敏感幅度调制，而PGS则将径向和锥形谱基底增强到边缘和读出标量特征中。在金属Ag、共价Si、短程离子LiF/Li--F子集以及MD17/rMD17分子上，这种标量路径修正将MACE力误差降低了22--27%，能量误差降低了19--22%；在具有应力标签的系统中，应力误差降低了27--28%，代价约为额外5%的推理FLOPs。在Allegro和NequIP中方向一致的效果进一步表明，该修正可跨不同短程等变主干迁移，尽管效应大小仍依赖于架构。这些结果将标量路径保真度确立为短程等变原子间势的一个实用设计维度。