Modelling infrared spectra of the O-H stretches in liquid H₂O based on a deep learning potential, the importance of nuclear quantum effects

In this study, we have trained a deep learning (DL) potential for water using training datasets obtained from the DPLibrary (https://dplibrary.deepmd.net/). Subsequently, we conducted classical molecular dynamics simulation (DeePMD) and path-integral MD simulation (PI-DeePMD) of liquid water based on this deep potential (DP). Using the velocity-velocity auto-correlation function (VVAF) formula, we constructed infrared (IR) spectra for the O-H stretching mode based on the DeePMD simulation trajectories. Our results showed that the DeePMD/VVAF approach accurately captured the experimental result for O-H stretching vibration, with the O-H vibrational peak located at 3400 cm⁻¹. Additionally, the PI-DeePMD approach successfully predicted the red-shift and broadening of the O-H stretching band of water, emphasising the importance of nuclear quantum effects (NQEs). In particular, the PI-DeePMD simulation correctly reproduced the shoulder located at 3250 cm⁻¹, which was underestimated by classical DeePMD simulation. The success of the PI-DeePMD/VVAF approach can be attributed to the following reasons: (1) the DeePMD simulation improves the accuracy of calculating atomic velocities due to the DFT-level accuracy of the DP model; (2) the PI-DeePMD simulation takes into account the contribution from nuclear quantum effects; (3) the non-Condon effect is considered in the VVAF formalism.