Vibrational infrared and Raman spectra of the methanol molecule with equivariant neural-network property surfaces

TL;DR

This paper develops high-accuracy vibrational spectra of methanol using ab initio data and equivariant neural networks, enabling detailed quantum dynamics modeling and astrophysical linelist generation.

Contribution

It introduces a novel combination of ab initio electronic structure data with equivariant neural networks to accurately compute vibrational spectra of methanol, including large-amplitude motions.

Findings

Vibrational energies and wave functions agree within 2.2 cm$^{-1}$ of experimental data.

All vibrational fundamentals, overtones, and combination bands match experimental results.

The method extends vibrational calculations up to the O-H stretching fundamental.

Abstract

Electric dipole and polarizability surfaces are developed for the methanol (CH$_3$OH) molecule using ab initio electronic structure data, computed at the CCSD/aug-cc-pVTZ level of theory, and equivariant neural networks. These property surfaces are used to compute vibrational infrared and Raman intensities with variational vibrational energies and wave functions. The energies and wave functions, fully accounting for the large-amplitude motion and tunneling splitting states, are from continued variational vibrational computations, based on earlier work [Sunaga et al., J. Chem. Phys., 2025, 163, 064101], up to 3700 cm$^{-1}$ beyond the zero-point vibration, now reaching the O-H stretching fundamental. All vibrational fundamentals, combination and overtone bands are in excellent agreement with available (gas-phase) experimental data, with a 2.2 cm$^{-1}$ root-mean-squared deviation of the fundamentals from experiment. These developments constitute an important step towards a quantitative and comprehensive exact quantum dynamics model of the methanol molecule, and a linelist for astrophysical applications.