{
  "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.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.28935",
  "author_total": 9,
  "authors": [
    {
      "name": "Zetong Li"
    },
    {
      "name": "Zhuosong Xie"
    },
    {
      "name": "Hengyu Fan"
    },
    {
      "name": "Jiaao Yu"
    },
    {
      "name": "Qiyao Hua"
    },
    {
      "name": "Zheng Lu"
    },
    {
      "name": "Liming Xu"
    },
    {
      "name": "Juanni Wu"
    },
    {
      "name": "Honglin Li"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/awxhmpjzhh09m.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.28935",
  "fulltext_url": null,
  "id": "awxhmpjzhh09m",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.28935",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "physics.chem-ph"
    ],
    [
      "openalex",
      "2505|Machine Learning in Materials Science"
    ]
  ],
  "openalex": "W7214390704",
  "pdf_url": "https://arxiv.org/pdf/2609.28935",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-20",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/awxhmpjzhh09m.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:34:04Z",
      "native_id": "2609.28935",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.28935"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "arxiv:physics.chem-ph",
      "subfield": "physical"
    }
  ],
  "title": "Response-state Learning for Transferable Vibrational Spectroscopic Characterization with Electron Prior",
  "title_text": "Response-state Learning for Transferable Vibrational Spectroscopic Characterization with Electron Prior",
  "type": "preprint",
  "updated_at": "2026-10-30T12:34:04Z",
  "url": "https://research.draconitic.com/paper/awxhmpjzhh09m",
  "venue": "arXiv",
  "versions": []
}