Predicting lattice thermal conductivity via machine learning: a mini review

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.