Thermal transports of 2D phosphorous carbides by machine learning molecular dynamics simulations

Carbon phosphide is a newly discovered two-dimensional semiconductor material which wrinkles and has a significant carrier mobility. Due to lack an accurate force field, the use of molecular dynamics to study its phonon-dominated thermal conductivity which lead to inaccurate results. At present, the use of machine learning to construct a high-precision force field has become the mainstream research method to solve this problem. The main work of this study is to construct a comprehensive training sets for Phosphorus-Doped Graphene (PCn) (n = 3, 5, 6) and to use the fitted potential to calculate the related thermal properties. The research found that (PC5) exhibited anisotropic behavior, with a thermal conductivity of 106.6 Wm−1 K−1 in the y-direction and 63.6 Wm−1 K−1 in the x-direction. In comparison, (PC6) and (PC3) showed isotropic behavior, with thermal conductivity of approximately 104 Wm−1 K−1 and 76.83 Wm−1 K−1, respectively. Compared to monolayer graphene, the lower thermal conductivity of PCn is mainly attributed to phonon-phonon scattering effects, which are limited by the regular wrinkled structure. Additionally, low-frequency phonon have been found to have a significant impact on the thermal performance of PCn. Furthermore, we investigated the influence of uniaxial strain on the PC6 and observed an increase in the thermal conductivity with increasing strain. This study used key computational and analytical techniques, including phonon dispersion relations, homogeneous nonequilibrium molecular dynamics method, spectral thermal conductivity analysis. These findings provide a theoretical basis for understanding the thermal transport properties of PCn and will guide its potential applications value.