Exploring the Premelting Transition through Molecular Simulations Powered by Neural Network Potentials

Abstract

The premelting layer on crystal surfaces significantly affects the stability, surface reactivity, and phase transition behaviors of crystals. Traditional methods for studying this layer—experimental techniques, classical simulations, and even first-principle simulations—have significant limitations in accuracy and scalability. To overcome these challenges, we employ molecular dynamic simulations based on neural network potentials to investigate the structural and dynamic behavior of the premelting layer on ice. This approach matches the accuracy of first-principle calculations while greatly improving computational efficiency, allowing us to simulate the ice–vapor interface on a much larger scale. In this study, we conducted a one-nanosecond simulation of the ice–vapor interface involving 1024 water molecules. This significantly exceeds the time and size scales of previous first-principle studies. Our simulation results indicate complete surface melting. Furthermore, our simulation results reveal dynamic heterogeneity within the premelting layer, with molecules segregated into clusters of low and high mobility.