Accurate estimation of interfacial thermal conductance between silicon and diamond enabled by a machine learning interatomic potential

TL;DR

This paper demonstrates that machine learning interatomic potentials, trained on DFT data, can accurately estimate interfacial thermal conductance between silicon and diamond, outperforming traditional potentials and aligning well with experimental results.

Contribution

The study introduces a machine learning interatomic potential for silicon-diamond interfaces that improves accuracy and efficiency in thermal conductance calculations compared to semi-empirical models.

Findings

ML potential closely matches experimental ITC data

Traditional potentials overestimate ITC by about 3 times

ML potential accurately captures phonon properties

Abstract

Thermal management at silicon-diamond interface is critical for advancing high-performance electronic and optoelectronic devices. In this study, we calculate the interfacial thermal conductance between silicon and diamond using machine learning (ML) interatomic potentials trained on density functional theory (DFT) data. Using non-equilibrium molecular dynamics (NEMD) simulations, we compute the interfacial thermal conductance (ITC) for various system sizes. Our results show a closer agreement with experimental data than those obtained using traditional semi-empirical potentials such as Tersoff and Brenner which overestimate ITC by a factor of about 3. In addition, we analyze the frequency-dependent heat transfer spectrum, providing insights into the contributions of different phonon modes to the interfacial thermal conductance. The ML potential accurately captures the phonon dispersion relations and lifetimes, in good agreement with DFT calculations and experimental observations. It is shown that the Tersoff potential predicts higher phonon group velocities and phonon lifetimes compared to the DFT results. Furthermore, it predicts higher interfacial bonding strength, which is consistent with higher interfacial thermal conductance as compared to the ML potential. This study highlights the use of the ML interatomic potential to improve the accuracy and computational efficiency of thermal transport simulations in complex material systems.