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Simulation of Phase-Change-Memory and Thermoelectric Materials using Machine-Learned Interatomic Potentials: Sb2Te3
Physica Status Solidi (B) - Basic Solid State Physics ( IF 1.5 ) Pub Date : 2020-11-17 , DOI: 10.1002/pssb.202000416
Konstantinos Konstantinou 1 , Juraj Mavračić 1 , Felix C. Mocanu 1 , Stephen R. Elliott 1
Affiliation  

Density-functional-theory (DFT)-based, ab initio molecular dynamics (AIMD) simulations of amorphous materials generally suffer from three computer-resource-related limitations due to their O(N3) cubic scaling with model system size, N. They are limited to a maximum model size of N ≈500 atoms; they are limited to time scales <1 ns; and, usually, only a single model can be simulated in any one investigation. This article discusses a machine-learned, linear-scaling (O(N)), DFT-accurate interatomic potential (a Gaussian approximation potential, GAP), originally developed by Mocanu et al. [J. Phys. Chem. B 2018, 122, 8998] using a Gaussian process regression method for the ternary phase-change-memory material Ge2Sb2Te5 (GST). The chemical transferability of this GAP potential is explored in an application to the case of simulating amorphous models of the phase-change-memory and thermoelectric material Sb2Te3, an end-member of the GST compositional tie-line GeTe–Sb2Te3. The GAP-model results are compared with those obtained from conventional DFT-based AIMD simulations.

中文翻译:

使用机器学习的原子间势模拟相变存储器和热电材料:Sb2Te3

无定形材料的基于密度泛函理论 (DFT) 的从头分子动力学 (AIMD) 模拟通常受到三个计算机资源相关的限制,因为它们的 O( N 3 ) 立方缩放与模型系统大小N。它们的最大模型大小限制为N ≈500 个原子;它们仅限于 <1 ns 的时间尺度;通常,在任何一项调查中只能模拟一个模型。本文讨论了最初由 Mocanu 等人开发的机器学习、线性标度 (O( N ))、DFT 精确的原子间势(高斯近似势,GAP)。[ J.物理。化学 乙2018, 122, 8998] 对三元相变存储材料 Ge 2 Sb 2 Te 5 (GST)使用高斯过程回归方法。在模拟相变记忆和热电材料 Sb 2 Te 3 的非晶模型(GST 成分连接线 GeTe-Sb 2 Te的末端成员)的应用中,探索了这种 GAP 电位的化学转移性3 . 将 GAP 模型结果与从传统的基于 DFT 的 AIMD 模拟中获得的结果进行比较。
更新日期:2020-11-17
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