Predicting lattice thermal conductivity via machine learning: a mini review  

作　　者：Yufeng Luo Mengke Li Hongmei Yuan Huijun Liu Ying Fang 

机构地区：[1]Key Laboratory of Artificial Micro-and Nano-Structures of Ministry of Education and School of Physics and Technology,Wuhan University,Wuhan 430072,China [2]School of Computer Science,Wuhan University,Wuhan 430072,China

出　　处：《npj Computational Materials》2023年第1期2322-2332,共11页计算材料学（英文）

基　　金：We thank financial support from the National Natural Science Foundation of China(Grant No.62074114).

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(κ_(L)),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 predictκL in a high-throughput style.In this review,we give some introductions of recent ML works on the direct and indirect prediction ofκL,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.

关 键 词：CONDUCTIVITY LATTICE THERMAL 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]