Abstract Scope |
The lattice thermal conductivity (k_L) is a critical property of thermoelectrics, thermal barrier coating materials and semiconductors. While accurate empirical measurements of k_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 k_L of inorganic materials, using a benchmark data set of experimentally measured k_L of about 100 inorganic materials. We use advanced and universal feature engineering techniques along with the Gaussian process regression algorithm, and compare the performance of our ML model with past theoretical works. The trained ML model is helpful for rational design and screening of novel materials. |