Structure and lattice thermal conductivity of grain boundaries in silicon by using machine learning potential and molecular dynamics

TL;DR

This paper uses machine learning potentials and molecular dynamics to accurately predict grain boundary structures in silicon and investigates their impact on lattice thermal conductivity, revealing anharmonic vibrations as a key factor.

Contribution

It introduces a machine learning potential-based approach for predicting silicon grain boundary structures and analyzes their effect on thermal conductivity with large-scale simulations.

Findings

MLPs enable accurate GB structure predictions.

Thermal conductivity depends on anharmonic vibrations at GBs.

MLPs outperform empirical potentials in modeling GBs.

Abstract

In silicon, lattice thermal conductivity plays an important role in a wide range of applications such as thermoelectric and microelectronic devices. Grain boundaries (GBs) in polycrystalline silicon can significantly reduce lattice thermal conductivity, but the impact of GB atomic structures on it remains to be elucidated. This study demonstrates accurate predictions of the GB structures, GB energies, and GB phonon properties in silicon using machine learning potentials (MLPs). The results indicate that the MLPs enable robust GB structure searches owing to the fact that the MLPs were developed from a training dataset covering a wide variety of structures. We also investigate lattice thermal conduction at four GB atomic structures using large-scale perturbed molecular dynamics and phonon wave-packet simulations. The comparison of these results indicates that the GB structure dependence of thermal conductivity stems from anharmonic vibrations at GBs rather than from the phonon transmission behavior at GBs. The advantages of the MLPs compared with a typical empirical potential of silicon are also thoroughly investigated.