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

Manipulating thermal conductivity (k) 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 thermal conductivity of fluorinated graphene (FG). We show that the lattice of FG can be significantly decreased with Bernal bilayer stacking. Surprisingly, the further increasing of stacking layer can no longer affect the thermal conductivity, however, the is increased in the bulk configuration. The variation of thermal conductivity 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 thermal conductivity. 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 thermal conductivity.