Thermal conductivity of GeTe crystals based on machine learning potentials

doi:10.1088/1674-1056/ad1b42

Abstract GeTe has attracted extensive research interest for thermoelectric applications. In this paper, we first train a neuro-evolution potential (NEP) based on a dataset constructed by ab initio molecular dynamics, with the Gaussian approximation potential (GAP) as a reference. The phonon density of states is then calculated by two machine learning potentials and compared with density functional theory results, with the GAP potential having higher accuracy. Next, the thermal conductivity of a GeTe crystal at 300 K is calculated by the equilibrium molecular dynamics method using both machine learning potentials, and both of them are in good agreement with the experimental results; however, the calculation speed when using the NEP potential is about 500 times faster than when using the GAP potential. Finally, the lattice thermal conductivity in the range of 300 K—600 K is calculated using the NEP potential. The lattice thermal conductivity decreases as the temperature increases due to the phonon anharmonic effect. This study provides a theoretical tool for the study of the thermal conductivity of GeTe.

Keywords: machine learning potentials thermal conductivity molecular dynamics

Received: 13 November 2023
Revised: 23 December 2023
Accepted manuscript online: 05 January 2024

PACS:	74.70.Dd	(Ternary, quaternary, and multinary compounds)
	74.25.fc	(Electric and thermal conductivity)
	74.25.Ha	(Magnetic properties including vortex structures and related phenomena)
	74.62.-c	(Transition temperature variations, phase diagrams)

Fund: Project supported by the A*STAR Computational Resource Centre through the use of its high-performance computing facilities. J. Zhang gratefully acknowledges financial support from the China Scholarship Council (Grant No. 202206120136) and is grateful to Prof. Dorde Dangic for his guidance on the use of GAP.

Corresponding Authors: Hao-Chun Zhang, Hao-Chun Zhang
E-mail: hczhang@hit.edu.cn;zhangg@ihpc.a-star.edu.sg

Cite this article:

Jian Zhang(张健), Hao-Chun Zhang(张昊春), Weifeng Li(李伟峰), and Gang Zhang(张刚) Thermal conductivity of GeTe crystals based on machine learning potentials 2024 Chin. Phys. B 33 047402

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