Machine Learning for Predicting Thermal Transport Properties of Solids

TL;DR

This review discusses how machine learning advances the prediction and design of thermal transport properties in solids, addressing limitations of traditional methods and exploring future opportunities.

Contribution

It provides a comprehensive overview of recent machine learning applications in modeling thermal conductivity, highlighting new approaches for high-throughput screening and structural design.

Findings

Machine learning improves modeling of phonon transport in defective and amorphous materials.

ML enables high-fidelity interatomic potentials bridging first-principles and molecular dynamics.

ML facilitates structural design for targeted thermal properties.

Abstract

Quantitative descriptions of the structure-thermal property correlation have been a bottleneck in designing materials with superb thermal properties. In the past decade, the first-principles phonon calculations using density functional theory and the Boltzmann transport equation have become a common practice for predicting the thermal conductivity of new materials. However, first-principles calculations are too costly for high-throughput material screening and multi-scale structural design. First-principles calculations also face several fundamental challenges in modeling thermal transport properties, e.g., of crystalline materials with defects, of amorphous materials, and for materials at high temperatures. In the past five years, machine learning started to play a role in solving these challenges. This review provides a comprehensive summary and discussion on the state-of-the-art, future opportunities, and the remaining challenges in implementing machine learning for studying thermal conductivity. After an introduction to the working principles of machine learning and descriptors of material structures, recent research using machine learning to study thermal transport is discussed. Three major applications of machine learning for predicting thermal properties are discussed. First, machine learning is applied to solve the challenges in modeling phonon transport of crystals with defects, in amorphous materials, and at high temperatures. Machine learning is used to build high-fidelity interatomic potentials to bridge the gap between first-principles calculations and molecular dynamics simulations. Second, machine learning can be used to study the correlation between thermal conductivity and other properties for high-throughput materials screening. Finally, machine learning is a powerful tool for structural design to achieve target thermal conductance or thermal conductivity.