An integrated computational approach for determining the elastic properties of boron nitride nanotubes

An integrated computational approach is proposed to investigate the compressive strength of boron nitride nanotubes (BNNTs). In this approach, an artificial intelligence (AI) cluster comprising of multi-gene genetic programming and molecular dynamics (MD) simulation technique, was specifically designed to formulate the explicit relationship of compressive strength of BNNTs with respect to system aspect ratio (AR), temperature and vacancy defects. It was found that the novel MD based AI model is able to model the compressive strength of BNNTs very well, which is in good agreement with that of experimental results obtained from the literature. Additionally, we also conducted sensitivity and parametric analysis to find out specific influence and variation of each of the input system parameters on the compressive strength of BNNTs. It was found that the AR has the most dominating influence on the compressive strength of BNNTs.