  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.
