Response-state Learning for Transferable Vibrational Spectroscopic Characterization with Electron Prior
Summary
This paper introduces SO(3) Equivariant Neural Kalman Networks (SENK) for accurate and transferable vibrational spectral prediction, outperforming existing methods and integrating tensor prediction with physics-informed calibration.
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# Response-state Learning for Transferable Vibrational Spectroscopic Characterization with Electron Prior Source: [https://arxiv.org/abs/2609.28935](https://arxiv.org/abs/2609.28935) [View PDF](https://arxiv.org/pdf/2609.28935) > Abstract: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\. ## Submission history From: Zetong Li \[[view email](https://arxiv.org/show-email/07b7edd4/2609.28935)\] **\[v1\]**Thu, 24 Sep 2026 02:36:10 UTC \(2,295 KB\)
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