Hall 123

Polymer knots and their predictions using machine learning

Sumanta Kundu

SISSA, Italy

Knots are topological states rigorously identified in closed curves using polynomial invariants. These knots occur naturally in sufficiently long swollen ring polymers or globular ones. The occurrence of knots is known to affect the static, dynamic, and rheological properties of the polymer while, on the other hand, knot formation and knot complexity strongly depend on the physical properties of the polymer itself (e.g., the stiffness). In this talk, I will present how and to what extent the supervised machine learning approaches can be employed to predict the topologically different knotted states of fully flexible ring polymers confined inside a spherical cavity. I will discuss the factors that may influence the predictions, such as the degree of spherical confinement, the length of the polymer chain of the tested configurations (shorter or longer than the trained ones), the topological family (torus/twist) of the knotted states, etc. Finally, I will discuss its applicability in recognizing the physical knots tied in open linear polymer chains.