Infrared spectroscopy of protonated water clusters via the quantum thermal bath method and highly accurate machine-learned potentials

TL;DR

This study employs machine-learned potentials and the quantum thermal bath method to efficiently simulate IR spectra of protonated water clusters, offering a cost-effective alternative to traditional high-level quantum methods.

Contribution

It introduces a novel combination of machine-learned PES and DMS with QTB for accurate, low-cost IR spectral simulations of water clusters from monomer to tetramer.

Findings

Accurate IR spectra obtained with reduced computational cost.

Validation against experimental and previous theoretical results.

Effective inclusion of nuclear quantum effects in simulations.

Abstract

The spectral features of water clusters provide important information on their structure and dynamics and can assist in deciphering the nature of the local environment of aqueous solutions in a variety of different conditions. Accurately capturing these features via numerical simulations is a non-trivial task that typically requires a sophisticated combination of high-level electronic structure methods and costly quantum dynamics techniques. We present results of molecular dynamics simulations of the IR spectra of protonated water clusters, ranging from the monomer to the tetramer, obtained via the combination of highly accurate machine-learned potential energy surfaces (PES) and dipole moment surfaces (DMS), and the quantum thermal bath (QTB) methodology which facilitates cost-effective inclusion of NQEs in molecular dynamics simulations. We compare our results with previous theoretical and experimental studies and show that this combination provides a significantly cheaper, yet still suitably accurate, alternative to more traditional computational approaches.