Machine learning for accelerated prediction of lattice thermal conductivity at arbitrary temperature

Abstract

Efficient evaluation of lattice thermal conductivity (κ_L) is critical for applications ranging from thermal management to energy conversion. In this work, we propose a neural network (NN) model that allows ready and accurate prediction of the κ_L of crystalline materials at arbitrary temperature. It is found that the data-driven model exhibits a high coefficient of determination between the real and predicted κ_L. Beyond the initial dataset, the strong predictive power of the NN model is further demonstrated by checking several systems randomly selected from previous first-principles studies. Most importantly, our model can realize high-throughput screening on countless systems either inside or beyond the existing databases, which is very beneficial for accelerated discovery or design of new materials with desired κ_L.