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粘度,斯托克斯-爱因斯坦关系的分解,以及超冷液体Ge2Sb2Te5中的动态异质性,来自与神经网络潜力的模拟
Simone Marcorini1, Rocco Pomodoro1, Omar Abou El Kheir1
1Department of Materials Science, University of Milano-Bicocca, Via R. Cozzi 55, I-20125 Milano, Italy.
The Journal of chemical physics
|October 15, 2025
概括
像Ge2Sb2Te5这样的相变材料对于电子记忆至关重要. 这项研究揭示了超冷液相中的原子动力学,解释了记忆性能并揭示了Ge原子的作用.
科学领域:
- 材料科学 材料科学 材料科学
- 凝聚物质物理学 凝聚物质物理学
- 计算化学的计算化学
背景情况:
- 阶段变换材料 (PCM) 通过可逆无形晶体转换使非挥发性电子记忆和光子设备成为可能.
- 设备的运行包括从超冷的液体相到玻璃过渡温度 (Tg) 以上的再结晶.
- 了解超冷液态动力学对于设备性能和玻璃结构放松至关重要.
研究的目的:
- 计算Ge2Sb2Te5.5的粘度 (η),扩散系数 (D) 和α-放松时间.
- 为了研究超冷液体的脆弱性和斯托克斯-爱因斯坦关系分解.
- 想象动态异质性及其与原子环境的关系.
主要方法:
- 机器学习对Ge2Sb2Te5.5的原子间潜力
- 大规模的分子动力学模拟.
- 异构配置分析. 同构配置分析.
主要成果:
- 在广泛的温度范围内计算了η,D和α放松时间.
- 在超冷的阶段量化液体脆弱性和观察到的斯托克斯-爱因斯坦关系分解.
- 将原子的移动性与Ge原子的特定局部环境联系在一起,揭示了动态异质性.
结论:
- 机器学习潜力可以准确模拟PCM动态.
- 动态异质性和斯托克斯-爱因斯坦分解是超冷 Ge2Sb2Te5.5 的关键特征.
- 原子局部环境显著影响原子的移动性和内存设备的行为.
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