Study of vacancy ordering and the boson peak in metastable cubic Ge-Sb-Te using machine learning potentials
Young-Jae Choi, Minjae Ghim, and Seung-Hoon Jhi

TL;DR
This study uses machine learning potentials to simulate vacancy ordering in cubic Ge-Sb-Te, revealing how vacancy arrangements influence lattice dynamics and the transition from amorphous-like to crystalline-like states.
Contribution
It demonstrates large-scale molecular dynamics simulations of vacancy ordering in c-GST using machine learning potentials, linking atomic arrangements to vibrational properties.
Findings
Vacancy ordering develops into a semi-ordered structure after annealing.
The boson peak disappears as vacancies become ordered.
Thermal annealing induces a transition from amorphous-like to crystalline-like dynamics.
Abstract
The mechanism of the vacancy ordering in metastable cubic Ge-Sb-Te (c-GST) that underlies the ultrafast phase-change dynamics and prominent thermoelectric properties remains elusive. Achieving a comprehensive understanding of the vacancy-ordering process at an atomic level is challenging because of enormous computational demands required to simulate disordered structures on large temporal and spatial scales. In this study, we investigate the vacancy ordering in c-GST by performing large-scale molecular dynamics simulations using machine learning potentials. The initial c-GST structure with randomly distributed vacancies rearranges to develop a semi-ordered cubic structure with layer-like ordered vacancies after annealing at 700~K for 100~ns. The vacancy ordering significantly affects the lattice dynamical properties of c-GST. In the initial structure with fully disordered vacancies, we…
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Taxonomy
TopicsAdvanced Thermoelectric Materials and Devices · Phase-change materials and chalcogenides · Machine Learning in Materials Science
