Layer-Stacking and Strain Effects on Heat Transport in Silicene

Over the past two decades, the unique transport phenomena of graphene and its derivatives have spurred research into various two-dimensional semiconducting materials. Among these, silicene shows promising electrical properties with potential applications in batteries, field-effect transistors, and gas sensors. One barrier to its integration in consumer devices is that its thermal behavior is not well understood in the context of strain and multilayer stacking. This work uses a machine-learning potential to investigate the effects of strain and layer-stacking on the lattice thermal conductivity of silicene using both molecular dynamics (MD) and anharmonic lattice dynamics (ALD). Our calculations predict an average value of κ=24.9 Wm^-1K^-1 for the in-plane thermal conductivity at room temperature and a mere 4% drop upon bilayer stacking. Analysis of the individual phonon modes in the bilayer reveals high-frequency acoustic modes with large group velocities, which make up for the large increase in scattering phase space. Finally, both MD and LD calculations of the monolayer display anomalous divergent heat transport upon biaxial strain in contrast with the bilayer for which thermal conductivity remains finite.