A molecular dynamics based artificial intelligence approach for characterizing thermal transport in nanoscale material

Abstract

A molecular dynamics (MD)-based-artificial intelligence (AI) simulation approach is proposed to investigate thermal transport of carbon nanotubes (CNTs). In this approach, the effect of size, chirality and vacancy defects on the thermal conductivity of CNTs is first analyzed using MD simulation. The data obtained using the MD simulation is then fed into the paradigm of an AI cluster comprising multi-gene genetic programming, which was specifically designed to formulate the explicit relationship of thermal transport of CNT with respect to system size, chirality and vacancy defect concentration. Performance of the proposed model is evaluated against the actual results. We find that our proposed MD-based-AI model is able to model the phenomenon of thermal conductivity of CNTs very well, which can be then used to complement the analytical solution developed by MD simulation. Based on sensitivity and parametric analysis, it was found that length has most dominating influence on thermal conductivity of CNTs.