Significant increase in thermal conductivity of cathode material LiFePO4 by Na substitution: A machine learning interatomic potential-assisted investigation

doi:10.1088/1674-1056/ad9e99

中国物理B ›› 2025, Vol. 34 ›› Issue (2): 28201-028201.doi: 10.1088/1674-1056/ad9e99

Significant increase in thermal conductivity of cathode material LiFePO₄ by Na substitution: A machine learning interatomic potential-assisted investigation

Shi-Yi Li(李诗怡), Qian Liu(刘骞), Yu-Jia Zeng(曾育佳), Guofeng Xie(谢国锋)†, and Wu-Xing Zhou(周五星)‡

School of Materials Science and Engineering, Hunan Provincial Key Laboratory of Advanced Materials for New Energy Storage and Conversion, Hunan University of Science and Technology, Xiangtan 411201, China

收稿日期:2024-09-26 修回日期:2024-11-29 接受日期:2024-12-13 出版日期:2025-02-15 发布日期:2025-01-15
通讯作者: Guofeng Xie, Wu-Xing Zhou E-mail:gfxie@xtu.edu.cn;wuxingzhou@hnu.edu.cn
基金资助:
This work was supported by the National Natural Science Foundation of China (Grant No. 12074115) and the Science and Technology Innovation Program of Hunan Province (Grant No. 2023RC3176).

Significant increase in thermal conductivity of cathode material LiFePO₄ by Na substitution: A machine learning interatomic potential-assisted investigation

Shi-Yi Li(李诗怡), Qian Liu(刘骞), Yu-Jia Zeng(曾育佳), Guofeng Xie(谢国锋)†, and Wu-Xing Zhou(周五星)‡

School of Materials Science and Engineering, Hunan Provincial Key Laboratory of Advanced Materials for New Energy Storage and Conversion, Hunan University of Science and Technology, Xiangtan 411201, China

Received:2024-09-26 Revised:2024-11-29 Accepted:2024-12-13 Online:2025-02-15 Published:2025-01-15
Contact: Guofeng Xie, Wu-Xing Zhou E-mail:gfxie@xtu.edu.cn;wuxingzhou@hnu.edu.cn
Supported by:
This work was supported by the National Natural Science Foundation of China (Grant No. 12074115) and the Science and Technology Innovation Program of Hunan Province (Grant No. 2023RC3176).

1. 2025-028201-SI.pdf(568KB)

摘要/Abstract

摘要： LiFePO$_{4}$ is a cathode material with good thermal stability, but low thermal conductivity is a critical problem. In this study, we employ a machine learning potential approach based on first-principles methods combined with the Boltzmann transport theory to investigate the influence of Na substitution on the thermal conductivity of LiFePO$_{4}$ and the impact of Li-ion de-embedding on the thermal conductivity of Li$_{3/4}$Na$_{1/4}$FePO$_{4}$, with the aim of enhancing heat dissipation in Li-ion batteries. The results show a significant increase in thermal conductivity due to an increase in phonon group velocity and a decrease in phonon anharmonic scattering by Na substitution. In addition, the thermal conductivity increases significantly with decreasing Li-ion concentration due to the increase in phonon lifetime. Our work guides the improvement of the thermal conductivity of LiFePO$_{4}$, emphasizing the crucial roles of both substitution and Li-ion detachment/intercalation for the thermal management of electrochemical energy storage devices.

关键词: lattice thermal conductivity, machine learning potential, LiFePO$_{4}$

Abstract: LiFePO$_{4}$ is a cathode material with good thermal stability, but low thermal conductivity is a critical problem. In this study, we employ a machine learning potential approach based on first-principles methods combined with the Boltzmann transport theory to investigate the influence of Na substitution on the thermal conductivity of LiFePO$_{4}$ and the impact of Li-ion de-embedding on the thermal conductivity of Li$_{3/4}$Na$_{1/4}$FePO$_{4}$, with the aim of enhancing heat dissipation in Li-ion batteries. The results show a significant increase in thermal conductivity due to an increase in phonon group velocity and a decrease in phonon anharmonic scattering by Na substitution. In addition, the thermal conductivity increases significantly with decreasing Li-ion concentration due to the increase in phonon lifetime. Our work guides the improvement of the thermal conductivity of LiFePO$_{4}$, emphasizing the crucial roles of both substitution and Li-ion detachment/intercalation for the thermal management of electrochemical energy storage devices.

Key words: lattice thermal conductivity, machine learning potential, LiFePO$_{4}$

中图分类号: (Lithium-ion batteries)

82.47.Aa

74.25.fc (Electric and thermal conductivity) 44.10.+i (Heat conduction) 66.70.-f (Nonelectronic thermal conduction and heat-pulse propagation in solids;thermal waves)

Shi-Yi Li(李诗怡), Qian Liu(刘骞), Yu-Jia Zeng(曾育佳), Guofeng Xie(谢国锋), and Wu-Xing Zhou(周五星). Significant increase in thermal conductivity of cathode material LiFePO₄ by Na substitution: A machine learning interatomic potential-assisted investigation[J]. 中国物理B, 2025, 34(2): 28201-028201.

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Significant increase in thermal conductivity of cathode material LiFePO₄ by Na substitution: A machine learning interatomic potential-assisted investigation

Significant increase in thermal conductivity of cathode material LiFePO₄ by Na substitution: A machine learning interatomic potential-assisted investigation

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