Machine learning potential for predicting thermal conductivity of {\theta}-phase and amorphous Tantalum Nitride

TL;DR

This paper develops deep potential models for TaN in different phases to accurately predict its thermal conductivity, addressing discrepancies between past experimental and theoretical results, and providing insights into thermal transport mechanisms.

Contribution

The study introduces deep potential models for { heta}-phase and amorphous TaN, enabling molecular dynamics simulations that improve understanding of thermal conductivity in these phases.

Findings

Simulation results align with experimental data.

Identified mechanisms affecting thermal transport.

Provided guidance for electronic device applications.

Abstract

Tantalum nitride (TaN) has attracted considerable attention due to its unique electronic and thermal properties, high thermal conductivity, and applications in electronic components. However, for the {\theta}-phase of TaN, significant discrepancies exist between previous experimental measurements and theoretical predictions. In this study, deep potential models for TaN in both the {\theta}-phase and amorphous phase were developed and employed in molecular dynamics simulations to investigate the thermal conductivities of bulk and nanofilms. The simulation results were compared with reported experimental and theoretical results, and the mechanism for differences were discussed. This study provides insights into the thermal transport mechanisms of TaN, offering guidance for its application in advanced electronic and thermal management devices.