Nuclear quantum effects at the liquid/vapor interface from neural-network based path integral molecular dynamics simulations

Abstract

Nuclear quantum effects (NQEs) significantly influence the properties of water, including its structure, dynamics, and phase behavior. While their impact on bulk water has been extensively studied, their role at the liquid–vapor interface remains largely unexplored. In this work, we employ machine-learned neural network potentials trained on ab initio data to conduct large-scale path-integral molecular dynamics simulations at the RPBE-D3 level. Our results reveal that NQEs increase the surface tension, albeit marginally, shift the critical point to higher temperatures, and alter the orientational preferences of interfacial water molecules. This study provides the first direct quantification of the effect of NQEs on the surface tension of water. These findings highlight the fundamental role of quantum fluctuations in interfacial physics and underscore the necessity of including NQEs in accurate simulations of aqueous systems.