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

TL;DR

This paper develops a machine learning model to accurately and efficiently predict the lattice thermal conductivity of inorganic materials, addressing limitations of traditional computational methods and aiding material design.

Contribution

It introduces a Gaussian process regression-based ML model trained on experimental data, improving prediction accuracy and revealing key features influencing thermal transport.

Findings

ML model achieves high prediction accuracy

Identifies key features governing thermal conductivity

Outperforms some traditional theoretical approaches

Abstract

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.