Maximization and Minimization of Interfacial Thermal Conductance by Modulating the Mass Distribution of Interlayer

TL;DR

This study investigates how varying the mass distribution in an interlayer affects interfacial thermal conductance in atomic chains, revealing complex optimal patterns and mechanisms for thermal management in nanoelectronics.

Contribution

It introduces a novel approach combining nonequilibrium Green's function and machine learning to optimize interfacial thermal conductance through mass distribution modulation.

Findings

Maximum thermal conductance occurs at sinusoidal-like mass distributions.

The optimal mass distribution is more complex than previously assumed linear or exponential.

Phonon spectra analysis explains the abnormal optimal distribution patterns.

Abstract

Tuning interfacial thermal conductance has been a key task for the thermal management of nanoelectronic devices. Here, it is studied how the interfacial thermal conductance is great influenced by modulating the mass distribution of the interlayer of one-dimensional atomic chain. By nonequilibrium Green's function and machine learning algorithm, the maximum/minimum value of thermal conductance and its corresponding mass distribution are calculated. Interestingly, the mass distribution corresponding to the maximum thermal conductance is not a simple function, such as linear and exponential distribution predicted in previous works, it is similar to a sinusoidal curve around linear distribution for larger thickness interlayer. Further, the mechanism of the abnormal results is explained by analyzing the phonon transmission spectra and density of states. The work provides deep insight into optimizing and designing interfacial thermal conductance by modulating mass distribution of interlayer atoms.