Table of Contents
Fetching ...

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

T. Baird, R. Vuilleumier, S. Bonella

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.

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

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.
Paper Structure (17 sections, 13 equations, 11 figures, 1 table)

This paper contains 17 sections, 13 equations, 11 figures, 1 table.

Figures (11)

  • Figure 1: IR spectrum of a single water molecule in the gas phase at 300K. Classical results are shown in green while the spectrum obtained via QTB is shown in blue. Scaling of the y-axis is the same as in benson2020quantum.
  • Figure 2: IR spectrum of the protonated water dimer in the gas phase at 300K. Our results are scaled with respect to the maximum peak in our computed spectra so that the y-axis is in arbitrary units and has a maximum value of 1.Classical results are shown in green while the spectrum obtained via QTB is shown in blue.
  • Figure 3: IR spectrum of the protonated water dimer in the gas phase at 20K. This lower temperature was considered in order to investigate whether the doublet structure observed in the MCTDH spectrum of H(H2O)_2+ at around 1700 cm${}^{-1}$ would emerge in the classical and QTB spectra when thermal effects are reduced. Classical results are shown in green while the spectrum obtained via QTB is shown in blue.
  • Figure 4: IR spectrum of the protonated water trimer in the gas phase. All simulations were performed at 100K. Classical results are shown in green while the spectrum obtained via QTB is shown in blue.
  • Figure 5: IR spectrum of the protonated water trimer in the gas phase at 300K.Classical results are shown in green while the spectrum obtained via QTB is shown in blue.
  • ...and 6 more figures