Illuminating Stress and Failure in Polyethylene with a Neural Network Potential

Polyethylene is a material with a range of applications which depend less on its intrinsic chemistry and more upon its structure on the nano- to micro-meter scale. Low-density polyethylene, with its more open, disordered structure and yields softer, more ductile materials. High-density and ultra-high molecular weight polyethylene (UHMWPE) can be used to make materials strong enough to for body armor and soldier protection. It is therefore paramount to understand how the mechanical properties of UHMWPE depend on its microstructure and how defects can impair the strength and lead to material failure. This is a difficult task however, given that we require a potential which can model bond breaking while being efficient and accurate enough to simulate bulk PE over nanosecond timescales. We have thus trained a neural network potential (NNP) based on the SCAN density functional potential energy surface which yields energies within 1 meV/atom of the ab-initio results yet is several orders of magnitude more efficient. We have used this potential to study the mechanical properties and failure of single PE strands and a PE crystal both for pristine samples and in the presence of defects, such as a single PE knot. We find that failure occurs depend on bond length deviations from the instantaneous average, not necessarily on the bond length alone, and that failure in knotted PE occurs at the knot entrance, similar to previous results by Klein and Saitta. Our results shed light on failure mechanisms in UHMWPE, how they are related to the material microstructure, and how they might be mitigated to yield improved properties.