Structure and Dynamics of Supercooled Liquid Ge2Sb2Te5 from Machine‐Learning‐Driven Simulations,Physica Status Solidi-Rapid Research Letters

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Structure and Dynamics of Supercooled Liquid Ge2Sb2Te5 from Machine‐Learning‐Driven Simulations
Physica Status Solidi-Rapid Research Letters ( IF 2.5 ) Pub Date : 2020-10-14 , DOI: 10.1002/pssr.202000403
Yu-Xing Zhou _{1,

2} , Han-Yi Zhang _{1,

2} , Volker L. Deringer ₃ , Wei Zhang _{1,

2}

Affiliation

Studies of supercooled liquid phase‐change materials are important for the development of phase‐change memory and neuromorphic computing devices. Herein, a machine‐learning (ML)‐based interatomic potential for Ge₂Sb₂Te₅ (GST) to conduct large‐scale molecular dynamics simulations of liquid and supercooled liquid GST is used. A pronounced effect of the thermostat parameters on the simulation results is demonstrated, and it is shown how using a Langevin thermostat with optimized damping values can lead to excellent agreement with reference ab initio molecular dynamics (AIMD) simulations. Structural and dynamical analyses are presented, including the studies of radial and angular distributions, homopolar bonds, and the temperature‐dependent diffusivity. Herein, the usefulness of ML‐driven molecular dynamics for further studies of supercooled liquid GST, with length and timescales far exceeding those that are accessible to AIMD is demonstrated.

中文翻译：

机器学习驱动模拟的过冷液态Ge2Sb2Te5的结构和动力学

过冷液相变材料的研究对于相变存储器和神经形态计算设备的开发很重要。此处，基于机器学习（ML）的Ge ₂ Sb ₂ Te ₅的原子间电势（GST）进行液体和过冷液体GST的大规模分子动力学模拟。演示了恒温器参数对模拟结果的显着影响，并显示了如何使用具有最佳阻尼值的兰格文恒温器如何与参考从头算分子动力学（AIMD）模拟取得出色的一致性。提出了结构和动力学分析，包括对径向和角度分布，同极性键以及与温度有关的扩散率的研究。在此，证明了ML驱动的分子动力学对于进一步研究过冷液体GST的有用性，其长度和时间范围远远超过AIMD可以访问的范围。

更新日期：2020-10-14

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