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The lattice thermal conductivity ($\kappa_{\rm L} $) is a critical propertyof thermoelectrics, thermal barrier coating materials and semiconductors. Whileaccurate empirical measurements of $\kappa_{\rm L} $ are extremely challenging,it is usually approximated through computational approaches, such assemi-empirical models, Green-Kubo formalism coupled with molecular dynamicssimulations, and first-principles based methods. However, these theoreticalmethods are not only limited in terms of their accuracy, but sometimes becomecomputationally intractable owing to their cost. Thus, in this work, we build amachine learning (ML)-based model to accurately and instantly predict$\kappa_{\rm L}$ of inorganic materials, using a benchmark data set ofexperimentally measured $\kappa_{\rm L} $ of about 100 inorganic solids. We useadvanced and universal feature engineering techniques along with the Gaussianprocess regression algorithm, and compare the performance of our ML model withpast theoretical works. The trained ML model is not only helpful for rationaldesign and screening of novel materials, but we also identify key featuresgoverning the thermal transport behavior in non-metals.

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