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.
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BibTeX
@misc{li2026responsestate,
title = {{Response-state Learning for Transferable Vibrational Spectroscopic Characterization with Electron Prior}},
author = {Zetong Li and Zhuosong Xie and Hengyu Fan and Jiaao Yu and Qiyao Hua and Zheng Lu and Liming Xu and Juanni Wu and Honglin Li},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2609.28935},
eprint = {2609.28935},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.28935},
}
RIS
TY - UNPB
TI - Response-state Learning for Transferable Vibrational Spectroscopic Characterization with Electron Prior
AU - Zetong Li
AU - Zhuosong Xie
AU - Hengyu Fan
AU - Jiaao Yu
AU - Qiyao Hua
AU - Zheng Lu
AU - Liming Xu
AU - Juanni Wu
AU - Honglin Li
PY - 2026
DA - 2026/10/20
PB - arXiv
DO - 10.48550/arxiv.2609.28935
UR - https://arxiv.org/abs/2609.28935
AB - 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.
ER -