发表机构
East China Normal University（华东师范大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对振动光谱预测受立体电子环境扰动而失准的问题，提出SENK模型，融合等变Transformer、神经卡尔曼桥及NBO电子先验校准，在QM9S和QMe14S上超越DetaNet，实现可迁移的IR/拉曼光谱预测。
AI 中文摘要
当局域立体电子环境扰动中间响应态且高风险响应单元主导特征光谱指纹时，振动光谱预测可能变得不准确，使得跨外部化学空间的预测变得困难。SO(3)等变神经卡尔曼网络（SENK）形成响应态级联，结合了用于Hessian、偶极导数及极化率导数学习的等变Transformer主干、用于状态相关细化与可靠性感知的等变神经卡尔曼桥，以及基于NBO的电子先验通路，该通路将一致性正则化与有界、分支特定的引导光谱校准相结合。SENK在QM9S和QMe14S上优于DetaNet，同时保持从小分子到类药物系统的全光谱红外和拉曼保真度。SENK在具有复杂立体电子效应的生物分子系统中保持稳定，并选择性地改进光谱敏感特征。因此，它整合了张量预测、可靠性诊断和物理信息校准，支持从分子系统到功能分子材料的可迁移振动光谱学。
英文摘要
Vibrational spectral prediction can become inaccurate when localized stereoelectronic environments perturb intermediate response states and high-risk response units dominate characteristic spectral fingerprints, making prediction across external chemical space difficult. SO(3) Equivariant Neural Kalman Networks (SENK) form a response-state cascade that combines an equivariant transformer backbone for Hessian, dipole-derivative and polarizability-derivative learning, an Equivariant Neural Kalman bridge for state-dependent refinement and reliability sensing, and an NBO-informed electronic-prior pathway coupling consistency regularization with bounded, branch-specific guided spectral calibration. SENK outperforms DetaNet on QM9S and QMe14S while preserving full-spectrum IR and Raman fidelity from small molecules to drug-like systems. SENK remains stable and selectively improves spectrally sensitive features in biomolecular systems with complex stereoelectronic effects. It therefore integrates tensor prediction, reliability diagnosis and physics-informed calibration, supporting transferable vibrational spectroscopy from molecular systems to functional molecular materials.
