  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. 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 not only helpful for rational
design and screening of novel materials, but we also identify key features
governing the thermal transport behavior in non-metals.
