Simulating the isotropic Raman spectra of O–H stretching mode in liquid H2O based on a machine learning potential: the influence of vibrational couplings

Abstract

In this study, we trained a deep potential (DP) for H₂O, an accurate machine learning (ML) potential. We performed molecular dynamics (MD) simulations of liquid water using the DP model (or DeePMD simulations). Our results showed that the DP model exhibits DFT-level accuracy, and the DeePMD simulation is a promising approach for modeling the structural properties of liquid water. Based on the DeePMD simulation trajectories, we calculated the isotropic Raman spectra of the O–H stretching mode using the surface–specific velocity–velocity correlation function (ssVVCF), showing that the DeePMD/ssVVCF approach can correctly capture the bimodal characteristics of the experimental Raman spectra, with one peak located near 3400 cm⁻¹ and the other near 3250 cm⁻¹. The success of the DeePMD/ssVVCF approach should be credited to (1) the DFT-level accuracy of the DP model for H₂O, (2) the ssVVCF formulation considering the coupling between vibrational modes, and (3) non-Condon effects. Furthermore, the DeePMD simulations revealed that the anharmonic interactions between the coupled water molecules in the first and second hydration shells should play an essential role in the strong mixing of the H–O–H bending mode and the O–H stretching mode, leading to the delocalization of the O–H stretching band. In particular, increasing the strength of hydrogen bonds would enhance the bend–stretch coupling, leading to the red-shifting of the O–H vibrational spectra and the increase in the intensity of the shoulder around 3250 cm⁻¹.