Predicting lattice thermal conductivity via machine learning: a mini review

Abstract

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.