In the research of condensed matter, atomistic dynamic simulations play a
crucial role, particularly in revealing dynamic processes, phase transitions
and thermodynamic statistics macroscopic physical properties in systems such as
solids and liquids. For a long time, simulating complex and disordered liquids
has been a challenge compared to ordered crystalline structures. The primary
reasons for this challenge are the lack of precise force field functions and
the neglect of nuclear quantum effects. To overcome these two limits in
simulation of liquids, we use a deep potential (DP) with quantum thermal bath
(QTB) approach. DP is a machine learning model are sampled from density
functional theory and able to do large-scale atomic simulations with its
precision. QTB is a method which incorporates nuclear quantum effects by
quantum fluctuation dissipation. The application of this first principles
approach enable us to successfully describe the phase transition processes in
solid and liquid Gallium (Ga) as well as the associated dynamic phenomena. More
importantly, we obtain the thermodynamic properties of liquid Ga, such as
internal energy, specific heat, enthalpy change, entropy and Gibbs free energy,
and these results align remarkably well with experiments. Our research has
opened up a new paradigm for the study of dynamics and thermodynamics in
liquids, amorphous materials, and other disordered systems, providing valuable
insights and references for future investigations.Comment: 7 pages, 11 figures for maintext; 6pages, 8 figures for supplementary
material