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Predicting transferable vibrational spectra across diverse chemical space is hindered by localized stereoelectronic effects that distort intermediate response tensors like Hessians and polarizability derivatives. To resolve this, the authors developed SO(3) Equivariant Neural Kalman Networks (SENK), which couple an equivariant transformer backbone with an Equivariant Neural Kalman bridge and an electronic prior derived from Natural Bond Orbitals. The framework outperforms DetaNet on QM9S and QMe14S benchmarks, maintaining full-spectrum IR and Raman fidelity across both drug-like molecules and complex biomolecular systems.
Accurate vibrational spectroscopy across chemical space hinges not just on geometric equivariance, but on dynamically filtering intermediate response tensors with quantum electronic priors.
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.