Materials Informatics for Heat Transfer: Recent Progresses and Perspectives

TL;DR

This paper reviews recent advances in materials informatics techniques, such as machine learning and high-throughput screening, for discovering and designing crystalline materials with tailored heat transfer properties.

Contribution

It highlights the integration of informatics methods with experimental and simulation data to accelerate the discovery of thermal materials with desired conductivities.

Findings

Materials informatics enables efficient screening of thermal properties.

Bayesian optimization and Monte Carlo methods aid nanostructure design.

Progress demonstrates the utility of informatics in thermal material development.

Abstract

With the advances in materials and integration of electronics and thermoelectrics, the demand for novel crystalline materials with ultimate high/low thermal conductivity is increasing. However, search for optimal thermal materials is challenge due to the tremendous degrees of freedom in the composition and structure of crystal compounds and nanostructures, and thus empirical search would be exhausting. Materials informatics, which combines the simulation/experiment with machine learning, is now gaining great attention as a tool to accelerate the search of novel thermal materials. In this review, we discuss recent progress in developing materials informatics for heat transport: the exploration of crystals with high/low thermal conductivity via high-throughput screening, and nanostructure design for high/low thermal conductance using the Bayesian optimization and Monte Carlo tree search. The progresses show that the materials informatics method are useful for designing thermal functional materials. We end by addressing the remaining issues and challenges for further development.