Revealing Crystallization Mechanism of Gallium Arsenide by Machine Learning Molecular Dynamics Simulation

Abstract

Due to its characteristics of high electron mobility, moderate band gap, excellent radiation resistance, and high-frequency performance, gallium arsenide (GaAs) is widely used as an important semiconductor material in microelectronics and optoelectronics. Here, we systematically investigated the crystallization mechanism of GaAs by machine learning molecular dynamics simulation. By combining unbiased molecular dynamics simulations with deep neural network potentials, the melting temperature of GaAs crystals was predicted, revealing the crystallization mechanism and phase competition rules. The results show that under undercooling conditions, the cubic zinc blende (ZB) phase has a slower crystallization rate than hexagonal wurtzite (WZ), and its excellent thermodynamic stability makes it the dominant phase in the crystallization process. These factors ultimately determine that a GaAs melt forms a mixed crystal mainly composed of the ZB phase. Ga and As atoms cooperatively complete the lattice arrangement at the solid–liquid interface, and the short-range order is the core driving force for rapid crystal growth. Thus, the microscopic mechanism of the solid–liquid phase transition of GaAs has been probed by applying atomic-scale simulation methods, providing theoretical support for the design, preparation, and performance control of semiconductor materials.