Machine learning assisted understanding of the layer-thickness dependent thermal conductivity in fluorinated graphene

Abstract

Abstract Manipulating thermal conductivity (κ) plays vital role in high-performance thermoelectric conversion, thermal insulation and thermal management devices. In this work, we using the machine learning-based interatomic potential and the phonon Boltzmann transport equation to systematically investigate layer thickness dependent κ of fluorinated graphene (FG). We show that the lattice κ of FG can be significantly decreased with Bernal bilayer stacking. Surprisingly, the further increasing of stacking layer can no longer affect the κ, however, the κ is increased in the bulk configuration. The variation of κ can be attributed to the crystal symmetry change from P-3m1 (164) at single layer to P3m1 (156) at multilayer. The decreasing crystal symmetry from single layer to bilayer resulting stronger phonon scattering and thus leading a lower κ. Moreover, we also show that the contribution of acoustic mode to κ decreases with the increase of layers, while the contribution of optical mode to κ is increased with increasing layers. These results provide a further understanding for the phonon scattering mechanism of layer thickness dependent κ.