  The present research aims to provide a practical numerical tool for the
mechanical analysis of nanoscale trusses with similar accuracy to molecular
dynamics (MD). As a first step, MD simulations of uniaxial tensile and
compression tests of all possible chiralities of single-walled carbon nanotubes
up to 4 nm in diameter were performed using the AIREBO potential. The results
represent a dataset consisting of stress/strain curves that were then used to
develop a neural network that serves as a surrogate for a constitutive model
for all nanotubes considered. The cornerstone of the new framework is a
partially input convex integrable neural network. It turns out that convexity
enables favorable convergence properties required for implementation in the
classical nonlinear truss finite element available in Abaqus. This completes a
molecular dynamics-machine learning-finite element framework suitable for the
static analysis of large, nanoscale, truss-like structures. The performance is
verified through a comprehensive set of examples that demonstrate ease of use,
accuracy, and robustness.
