Understanding Strain and Failure of a Knot in Polyethylene Using Molecular Dynamics with Machine-Learned Potentials

A neural network potential (NNP) has been developed by
fitting
to ab initio electronic structure data on hydrocarbons and is used
to study failure of linear and knotted polyethylene (PE) chains. A
linear PE chain must be highly strained before breaking as the stress
is equally distributed across the chain. In contrast, the stress in
a PE chain with a 31 or overhand knot, accumulates at the
knot’s entrance/exit. We find the strain energy is greatest
when the bond length and angle are strained simultaneously, and that
the knot weakens the chain by increasing the variance of the C–C–C
angle, thereby allowing rupture at lower bond strains. We extend our
analysis to both 51 and 52 knots and find that
both break at the entrance/exit of a loop. Notably, molecular scale
PE knots exhibit many of the same characteristics as knots in a macroscopic
rope, with stick–slip phenomena upon tightening and similar
points of failure.