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

Area of Science:

• Computational materials science
• Polymer physics
• Molecular dynamics

Background:

• Polyethylene (PE) chains are fundamental polymers with diverse applications.
• Understanding the mechanical failure of polymers at the molecular level is crucial for material design.
• Knots can alter the mechanical properties of polymer chains in unpredictable ways.

Purpose of the Study:

• To investigate the mechanical failure of linear and knotted polyethylene chains using a neural network potential.
• To compare the failure mechanisms of different types of knots (31, 51, 52) in PE chains.
• To explore the similarities between molecular-scale PE knots and macroscopic rope failure.

Main Methods:

• Development of a neural network potential (NNP) fitted to ab initio electronic structure data for hydrocarbons.
• Simulations of linear and knotted polyethylene chains under strain.
• Analysis of stress distribution, strain energy, and bond/angle variance.

Main Results:

• Linear PE chains require high strain to break due to uniform stress distribution.
• Knotted PE chains (31, 51, 52) concentrate stress at the knot's entrance/exit, leading to earlier failure.
• Simultaneous strain in bond length and angle maximizes strain energy.
• Knots increase C-C-C angle variance, facilitating rupture at lower bond strains.
• Molecular knots exhibit stick-slip phenomena and failure points analogous to macroscopic ropes.

Conclusions:

• Knot topology significantly impacts the mechanical strength of polyethylene chains.
• The NNP model provides accurate insights into polymer chain failure mechanisms.
• The study reveals a compelling parallel between the failure of molecular and macroscopic knots.