Predicting lattice thermal conductivity via machine learning: a mini review

npj Computational Materials

Review Article

Mengke Li¹  Hongmei Yuan¹  Yufeng Luo¹  Huijun Liu¹  Ying Fang² 

[1] Key Laboratory of Artificial Micro- and Nano-Structures of Ministry of Education and School of Physics and Technology, Wuhan University, 430072, Wuhan, China;School of Computer Science, Wuhan University, 430072, Wuhan, China;

DOI  :  10.1038/s41524-023-00964-2

 received in 2022-10-19, accepted in 2023-01-02,  发布年份 2023

来源: Springer

【 摘 要 】

Over the past few decades, molecular dynamics simulations and first-principles calculations have become two major approaches to predict the lattice thermal conductivity (κL), which are however limited by insufficient accuracy and high computational cost, respectively. To overcome such inherent disadvantages, machine learning (ML) has been successfully used to accurately predict κL in a high-throughput style. In this review, we give some introductions of recent ML works on the direct and indirect prediction of κL, where the derivations and applications of data-driven models are discussed in details. A brief summary of current works and future perspectives are given in the end.

【 授权许可】

CC BY   
© The Author(s) 2023

【 图 表 】

Fig. 3

41116_2022_35_Article_IEq101.gif

41116_2022_35_Article_IEq99.gif

41116_2022_35_Article_IEq96.gif

Fig. 2

41116_2022_35_Article_IEq92.gif

Fig. 3

41116_2022_35_Article_IEq71.gif

12888_2022_4506_Article_IEq1.gif