Significant increase in thermal conductivity of cathode material LiFePO_(4) by Na substitution:A machine learning interatomic potential-assisted investigation  

作　　者：Shi-Yi Li Qian Liu Yu-Jia Zeng Guofeng Xie Wu-Xing Zhou 李诗怡;刘骞;曾育佳;谢国锋;周五星

机构地区：[1]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

出　　处：《Chinese Physics B》2025年第2期463-468,共6页中国物理B(英文版)

基　　金：supported by the National Natural Science Foundation of China(Grant No.12074115);the Science and Technology Innovation Program of Hunan Province(Grant No.2023RC3176)。

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 Li FePO_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) 

分 类 号：TG1[金属学及工艺—金属学]