Atomistic simulations of thermodynamic properties of liquid gallium from first principles
Hongyu Wu, Wenliang Shi, Ri He, Guoyong Shi, Chunxiao Zhang, Zhicheng Zhong, Run-wei Li

TL;DR
This paper introduces a first-principles atomistic simulation approach using deep potential and quantum thermal bath methods to accurately study thermodynamic properties and phase transitions of liquid gallium, aligning well with experimental data.
Contribution
The study develops a novel simulation framework combining machine learning potentials with quantum effects to investigate liquids, overcoming previous limitations in modeling disordered systems.
Findings
Successfully described phase transitions in gallium
Accurately computed thermodynamic properties matching experiments
Established a new paradigm for liquid and disordered system simulations
Abstract
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…
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Taxonomy
TopicsAdvanced Thermodynamics and Statistical Mechanics · Spectroscopy and Quantum Chemical Studies · Material Dynamics and Properties
