Predicting the mechanical properties of pristine and defective carbon nanotubes using a random forest model.

ABSTRACT: Data-driven models have lately emerged as a faster and less time-consuming method for computing material properties than computationally expensive conventional molecular dynamics and density functional theory-based simulations. Here, we developed a random forest (RF) model for comprehensively predicting mechanical properties such as stress and Poisson's ratio under varying strain and ultimate tensile strain of pristine and defective carbon nanotubes (CNTs). The variations in stress and Poisson's ratio with the strain of CNTs with a 0.4-2 nm diameter range were calculated by classical molecular dynamics simulations and characterized using parameters extracted from fitting polynomial equations. The fitting parameters and ultimate tensile strength showed distinct dependency on chiral indices,

SUBMITTER: Ibn Malek I 

PROVIDER: S-EPMC11337013 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature