Anisotropic Crystallization Kinetics and Interfacial Dynamics of Phase-Change Material Sb$_2$S$_3$ from Machine Learning Force Field Simulations
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| Format: | Preprint |
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2026
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| _version_ | 1866911700760920064 |
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| author | Chakraborty, Souvik Li, Wen-Qing Liu, Yun |
| author_facet | Chakraborty, Souvik Li, Wen-Qing Liu, Yun |
| contents | The phase-change material antimony sulfide (Sb$_2$S$_3$) 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 Sb$_2$S$_3$ for the first time, by enabling large-scale molecular dynamics simulations (up to 7680 atoms for 40 ns). Sb$_2$S$_3$ 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 activation energy for crystal growth indicates that its heterogeneous crystallization is interface controlled rather than diffusion limited, unlike GST and GeTe with atomic attachment at the solid-liquid interface being energetically favoured over long range atomic transport. These findings provide key insights into the structural, thermodynamic, and kinetic properties of Sb$_2$S$_3$, paving the way for optimizing its functionality including switching speed, reliability, and energy efficiency. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2605_20785 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Anisotropic Crystallization Kinetics and Interfacial Dynamics of Phase-Change Material Sb$_2$S$_3$ from Machine Learning Force Field Simulations Chakraborty, Souvik Li, Wen-Qing Liu, Yun Materials Science Disordered Systems and Neural Networks The phase-change material antimony sulfide (Sb$_2$S$_3$) 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 Sb$_2$S$_3$ for the first time, by enabling large-scale molecular dynamics simulations (up to 7680 atoms for 40 ns). Sb$_2$S$_3$ 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 activation energy for crystal growth indicates that its heterogeneous crystallization is interface controlled rather than diffusion limited, unlike GST and GeTe with atomic attachment at the solid-liquid interface being energetically favoured over long range atomic transport. These findings provide key insights into the structural, thermodynamic, and kinetic properties of Sb$_2$S$_3$, paving the way for optimizing its functionality including switching speed, reliability, and energy efficiency. |
| title | Anisotropic Crystallization Kinetics and Interfacial Dynamics of Phase-Change Material Sb$_2$S$_3$ from Machine Learning Force Field Simulations |
| topic | Materials Science Disordered Systems and Neural Networks |
| url | https://arxiv.org/abs/2605.20785 |