A data driven approach to model thermal boundary resistance from molecular dynamics simulations

Abstract

A new method is proposed to model the thermal boundary resistance (TBR) at the nanoscale, solid–liquid interface from macroscopic observables that characterize a nanoscale interface. We correlated the TBR with thermodynamic state variables, material properties, and geometric parameters to derive a generalized relationship with the help of data-driven heuristic algorithms. The results show that TBR can be expressed in terms of physical observables of the systems and material-specific parameters. We investigated the mutual independence of descriptor variables and quantified the weightage for each observable parameter in the TBR models. The interfacial liquid layering has a robust correlation with TBR. However, for systems with phonon size effects and under extreme thermodynamic conditions, the work of adhesion and system geometry also affects the variation in TBR. The data-driven approach followed in this study helps us gain better insight into the mechanism of TBR at nanoscale solid–liquid interfaces and shows significant improvement in our knowledge about interfacial thermal transport.