Atomistic simulations of thermodynamic properties with nuclear quantum effects of liquid gallium from first principles

Determining thermodynamic properties in disordered systems remains a formidable challenge because of the difficulty in incorporating nuclear quantum effects into large-scale and nonperiodic atomic simulations. In this study, we employ a machine learning deep potential model in conjunction with the quantum thermal bath method, enabling machine learning molecular dynamics to simulate thermodynamic quantities of liquid materials with satisfactory accuracy without significantly increasing computational costs. Using this approach, we accurately calculate the variations in various thermodynamic quantities of liquid metal gallium at temperatures ranging from zero to room temperature. The calculated thermodynamic properties accurately capture the solid-liquid phase transition behavior of gallium, whereas classical molecular dynamics methods fail to reproduce realistic results. Through this approach, we offer a potential method for accurately calculating the thermodynamic properties of liquids and other disordered systems.