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