Thermal conductivity of GeTe crystals based on machine learning potentials

doi:10.1088/1674-1056/ad1b42

中国物理B ›› 2024, Vol. 33 ›› Issue (4): 47402-047402.doi: 10.1088/1674-1056/ad1b42

所属专题： SPECIAL TOPIC — Heat conduction and its related interdisciplinary areas

Thermal conductivity of GeTe crystals based on machine learning potentials

Jian Zhang(张健)^1,2, Hao-Chun Zhang(张昊春)^1,†, Weifeng Li(李伟峰)³, and Gang Zhang(张刚)^2,‡

1 School of Energy Science and Engineering, Harbin Institute of Technology, Harbin 150001, China;
2 Institute of High Performance Computing, Agency for Science, Technology and Research(A*STAR), Singapore 138632, Singapore;
3 School of Physics & State Key Laboratory of Crystal Materials, Shandong University, Jinan 250100, China

收稿日期:2023-11-13 修回日期:2023-12-23 接受日期:2024-01-05 出版日期:2024-03-19 发布日期:2024-04-07
通讯作者: Hao-Chun Zhang, Hao-Chun Zhang E-mail:hczhang@hit.edu.cn;zhangg@ihpc.a-star.edu.sg
基金资助:
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.

Thermal conductivity of GeTe crystals based on machine learning potentials

Jian Zhang(张健)^1,2, Hao-Chun Zhang(张昊春)^1,†, Weifeng Li(李伟峰)³, and Gang Zhang(张刚)^2,‡

1 School of Energy Science and Engineering, Harbin Institute of Technology, Harbin 150001, China;
2 Institute of High Performance Computing, Agency for Science, Technology and Research(A*STAR), Singapore 138632, Singapore;
3 School of Physics & State Key Laboratory of Crystal Materials, Shandong University, Jinan 250100, China

Received:2023-11-13 Revised:2023-12-23 Accepted:2024-01-05 Online:2024-03-19 Published:2024-04-07
Contact: Hao-Chun Zhang, Hao-Chun Zhang E-mail:hczhang@hit.edu.cn;zhangg@ihpc.a-star.edu.sg
Supported by:
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.

摘要/Abstract

摘要： 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.

关键词: machine learning potentials, thermal conductivity, molecular dynamics

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.

Key words: machine learning potentials, thermal conductivity, molecular dynamics

中图分类号: (Ternary, quaternary, and multinary compounds)

74.70.Dd

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)

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[J]. 中国物理B, 2024, 33(4): 47402-047402.

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[J]. Chin. Phys. B, 2024, 33(4): 47402-047402.

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Thermal conductivity of GeTe crystals based on machine learning potentials

Thermal conductivity of GeTe crystals based on machine learning potentials

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