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WMS-Rot: From quantum-chemical predictions to rotational spectral assignment and refinement.
PubMed
Authors: Lazzari F, Barone V
Year
2026
Paper ID
69625
Status
Peer-reviewed
Abstract Read
~2 min
Abstract Words
156
Citations
N/A
Abstract
We present WMS-Rot and its fitting companion WMS-FitRot as an integrated framework for the early stages of rotational spectral analysis, starting from spectroscopic parameters obtained from electronic-structure computations and progressing to assignment-aware local refinement driven by the same theoretical catalog used for prediction. The framework provides a practical and internally consistent route connecting modern composite quantum-chemical predictions to first-pass assignments and controlled refinement. More fundamentally, it reformulates the incorporation of theoretical information into the spectroscopic inverse problem: calculated parameters act not only as initial guesses but also as active constraints that stabilize assignments and guide early-stage refinement within a unified simulation-fit cycle. Applications to nicotinic acid and thiopronine show that accurate composite inputs markedly improve starting points compared to low-level models, enabling robust assignment, reliable conformer discrimination, and consistent refinement. The approach reproduces matched reduced-Hamiltonian fits while remaining fully compatible with standard SPCAT/SPFIT practice and provides diagnostic insight into parameter correlations, identifiability, and model conditioning.
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- This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
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- We present WMS-Rot and its fitting companion WMS-FitRot as an integrated framework for the early stages of rotational spectral analysis, starting from spectroscopic parameters...
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