An embedded simulation approach for modeling the thermal conductivity of 2D nanoscale material

The thermal property of single layer graphene sheet is investigated in this work by using an embedded approach of molecular dynamics (MD) and soft computing method. The effect of temperature and Stone–Thrower–Wales (STW) defects on the thermal conductivity of graphene sheet is first analyzed using MD simulation. The data obtained using the MD simulation is then fed into the paradigm of soft computing approach, multi-gene genetic programming (MGGP), which was specifically designed to model the response of thermal conductivity of graphene sheet with changes in system temperature and STW defect concentration. We find that our proposed MGGP model is able to model the thermal conductivity of graphene sheet very well which can be used to complement the analytical solution developed by MD simulation. Additionally, we also conducted sensitivity and parametric analysis to find out specific influence and variation of each of the input system parameters on the thermal conductivity of graphene sheet. It was found that the STW defects has the most dominating influence on the thermal conductivity of graphene sheet.