AI and the World's Hard Problems · Chemistry & materials · arXiv · 2026-10-21
Response-state Learning for Transferable Vibrational Spectroscopic Characterization with Electron Prior
Vibrational spectral prediction becomes inaccurate when localized stereoelectronic environments perturb intermediate response states and high-risk response units dominate spectral fingerprints. SO(3) Equivariant Neural Kalman Networks (SENK) form a response-state cascade combining 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. SENK outperforms DetaNet on QM9S and QMe14S while preserving full-spectrum IR and Raman fidelity from small molecules to drug-like systems, and remains stable in biomolecular systems with complex stereoelectronic effects.