Machine Learning Models for the Lattice Thermal Conductivity Prediction of Inorganic Materials

Understand

The lattice thermal conductivity ($\kappa_{\rm L} $) is a critical property of thermoelectrics, thermal barrier coating materials and semiconductors.

• While accurate empirical measurements of $\kappa_{\rm L} $ are extremely challenging, it is usually approximated through computational approaches, such as semi-empirical models, Green-Kubo formalism coupled with molecular dynamics simulations, and first-principles based methods.
• However, these theoretical methods are not only limited in terms of their accuracy, but sometimes become computationally intractable owing to their cost.
• Thus, in this work, we build a machine learning (ML)-based model to accurately and instantly predict $\kappa_{\rm L}$ of inorganic materials, using a benchmark data set of experimentally measured $\kappa_{\rm L} $ of about 100 inorganic solids.