Descriptors of intrinsic hydrodynamic thermal transport: screening a phonon database in a machine learning approach

TL;DR

This paper employs machine learning to identify key descriptors influencing intrinsic hydrodynamic thermal transport in materials, enabling the screening of thousands of compounds for potential thermoelectric and heat dissipation applications.

Contribution

It introduces a comprehensive set of descriptors and a trained model to predict hydrodynamic thermal transport properties across a large material database.

Findings

Key descriptors related to phonon relaxation times influence hydrodynamic effects

Screened over 5000 materials to find candidates with desirable thermal properties

Identified materials suitable for thermoelectric and heat dissipation applications

Abstract

Machine learning techniques are used to explore the intrinsic origins of the hydrodynamic thermal transport and to find new materials interesting for science and engineering. The hydrodynamic thermal transport is governed intrinsically by the hydrodynamic scale and the thermal conductivity. The correlations between these intrinsic properties and harmonic and anharmonic properties, and a large number of compositional (290) and structural (1224) descriptors of 131 crystal compound materials are obtained, revealing some of the key descriptors that determines the magnitude of the intrinsic hydrodynamic effects, most of them related with the phonon relaxation times. Then, a trained black-box model is applied to screen more than 5000 materials. The results identify materials with potential technological applications. Understanding the properties correlated to hydrodynamic thermal transport can help to find new thermoelectric materials and on the design of new materials to ease the heat dissipation in electronic devices.