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

2026-09-23

AI Summary

This study addresses the inaccuracy in cross-domain vibrational spectroscopy prediction caused by stereoelectronic effects interfering with response states. To this end, we propose the SENK network coupled with a response-state cascaded architecture. Methodologically, the framework integrates an SO(3)-equivariant Transformer, a neural Kalman bridge, and natural bond orbital (NBO) electronic priors, while incorporating reliability diagnostics and physics-informed calibration mechanisms. Experimental evaluations on the QM9S and QMe14S benchmarks demonstrate that the proposed model outperforms DetaNet, achieving high-fidelity infrared and Raman spectral predictions spanning from small molecules to drug-sized systems. These results effectively overcome the spectral transfer bottleneck for complex molecular systems.

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.

Problem

• vibrational spectroscopy
• spectral prediction
• stereoelectronic effects
• transferability
• chemical space

Innovation

• Equivariant Neural Kalman Networks
• Vibrational Spectroscopy
• Electronic Prior
• Consistency Regularization
• Transferable Prediction