Response-state Learning for Transferable Vibrational Spectroscopic Characterization with Electron Prior

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.

Machine Learning (cs.LG); Chemical Physics (physics.chem-ph)

arXiv:2609.28935 [cs.LG]

(or arXiv:2609.28935v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.28935 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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