Anisotropic Crystallization Kinetics and Interfacial Dynamics of Phase-Change Material Sb$_2$S$_3$ from Machine Learning Force Field Simulations
Souvik Chakraborty, Wen-Qing Li, Yun Liu

TL;DR
This study employs machine learning force fields to simulate and analyze the anisotropic crystallization kinetics and interfacial dynamics of Sb$_2$S$_3$, revealing atomistic mechanisms and growth behaviors relevant for data storage and photonics.
Contribution
It introduces a machine learning force field for large-scale molecular dynamics simulations of Sb$_2$S$_3$, providing new insights into its crystallization kinetics and anisotropic growth.
Findings
Sb$_2$S$_3$ exhibits anisotropic growth rates with the fastest along the [100] facet.
Activation energy for crystal growth is approximately 0.55-0.57 eV.
Crystallization is interface-controlled rather than diffusion limited.
Abstract
The phase-change material antimony sulfide (SbS) relies on rapid and reversible phase transitions between crystalline and amorphous states, which are critical for their performance in data storage and photonics applications. In this work, a machine learning force field is developed based on the moment tensor potential approach, allowing us to understand the atomistic origin of the structural evolution and crystallization kinetics in SbS for the first time, by enabling large-scale molecular dynamics simulations (up to 7680 atoms for 40 ns). SbS shows anisotropic growth rates with the [100] facet exhibiting the fastest growth due to the strong Sb-S covalent bonding along its quasi-1D ribbon-like structure of its crystalline phase. The activation energy for crystal growth is found to be 0.55-0.57 eV, whereas that for diffusion is around 1.16-1.56 eV. The lower…
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