A study of the morphology, dynamics, and folding pathways of ring polymers with supramolecular topological constraints using molecular simulation and nonlinear manifold learning
Jiang Wang, Andrew Ferguson

TL;DR
This study uses molecular dynamics and nonlinear manifold learning to explore how topology and size influence the shape, dynamics, and folding pathways of ring polymers, providing insights for designing polymer-based nanodevices.
Contribution
It introduces a combined simulation and machine learning approach to analyze the effects of topology and polymer size on ring polymer behavior.
Findings
Topology significantly affects folding pathways.
Degree of polymerization influences conformational diversity.
Topological constraints impact rotational diffusion.
Abstract
Ring polymers are prevalent in natural and engineered systems, including circular bacterial DNA, crown ethers for cation chelation, and mechanical nanoswitches. The morphology and dynamics of ring polymers are governed by the chemistry and degree of polymerization of the ring, and intramolecular and supramolecular topological constraints such as knots or mechanically-interlocked rings. In this study, we perform molecular dynamics simulations of polyethylene ring polymers at two different degrees of polymerization and in different topological states, including a trefoil knot, catenane state (two interlocked rings), and Borromean state (three interlocked rings). We employ nonlinear manifold learning to extract the low-dimensional free energy surface to which the structure and dynamics of the polymer chain are effectively restrained. The free energy surfaces reveal how degree of…
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