A Stochastic Model for High Performance Computing of Polymer Gel Behavior

Hydrogels have attracted attention as “smart” materials for their tunable mechanical properties that respond to environmental stimuli such as pH, UV, or temperature making them ideal for a variety of biomedical and sensor technology applications. Consisting of mostly water, the Non-Newtonian and viscoelastic properties of hydrogels are the product of a network of long polymer chains attaching and detaching at various entanglement points. Due to this complexity at the micro-scale, many previous polymer simulations rely on mathematical simplifications of chain dynamics to produce results. In this poster we explore the potential for a mean-field model to capture the properties of hydrogels using massively parallel computation on graphics processing units. This approach abstracts away the network positions and uses stochastic differential equations derived from physical properties to capture the breaking and reforming behavior of attached segments, thereby keeping the non-linear micro-scale polymer dynamics intact.