Thermal transport of glasses via machine learning driven simulations

TL;DR

This paper reviews how machine learning potentials can be used in atomistic simulations to predict the thermal conductivity of glasses, addressing challenges in modeling disorder and composition effects.

Contribution

It provides a comprehensive review of recent methods and numerical applications of machine learning potentials for thermal transport in glasses.

Findings

MLPs enable accurate thermal conductivity predictions for glasses.

Numerical applications on vitreous silica and silicon demonstrate effectiveness.

MLPs bridge the gap between computational efficiency and disorder complexity.

Abstract

Accessing the thermal transport properties of glasses is a major issue for the design of production strategies of glass industry, as well as for the plethora of applications and devices where glasses are employed. From the computational standpoint, the chemical and morphological complexity of glasses calls for atomistic simulations where the interatomic potentials are able to capture the variety of local environments, composition, and (dis)order that typically characterize glassy phases. Machine-learning potentials (MLPs) are emerging as a valid alternative to computationally expensive ab initio simulations, inevitably run on very small samples which cannot account for disorder at different scales, as well as to empirical force fields, fast but often reliable only in a narrow portion of the thermodynamic and composition phase diagrams. In this article, we make the point on the use of MLPs to compute the thermal conductivity of glasses, through a review of recent theoretical and computational tools and a series of numerical applications on vitreous silica and vitreous silicon, both pure and intercalated with lithium.