Molecular dynamics studies of knotted polymers

Molecular dynamics calculations have been used to explore the influence of knots on the strength of a polymer strand. In particular, the mechanism of breaking 31, 41, 51, and 52 prime knots has been studied using two very different models to represent the polymer: (1) the generic coarse-grained (CG) bead model of polymer physics and (2) a state-of-the-art machine learned atomistic neural network (NN) potential for polyethylene derived from electronic structure calculations. While there is a broad overall agreement between the results on the influence of the pulling rate on chain rupture based on the CG and atomistic NN models, for the simple 31 and 41 knots, significant differences are found for the more complex 51 and 52 knots. Notably, in the latter case, the NN model more frequently predicts that these knots can break not only at the crossings at the entrance/exit but also at one of the central crossing points. The relative smoothness of the CG potential energy surface also leads to stabilization of tighter knots compared to the more realistic NN model.

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Publication Details

Journal
The Journal of Chemical Physics
Published
2024-12-23
DOI
https://doi.org/10.1063/5.0237773
Citations
3
Primary Topic
Force Microscopy Techniques and Applications
Type
article
Field-Weighted Citation Impact
1.01

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article

Molecular dynamics studies of knotted polymers

Michael L. Klein, Mark DelloStritto, Cristian Micheletti
3 citations
The Journal of Chemical Physics
Force Microscopy Techniques and Applications
1.01
article

Molecular dynamics studies of knotted polymers

Michael L. Klein, Mark DelloStritto, Cristian Micheletti
article en
3 citations

Abstract

Molecular dynamics calculations have been used to explore the influence of knots on the strength of a polymer strand. In particular, the mechanism of breaking 31, 41, 51, and 52 prime knots has been studied using two very different models to represent the polymer: (1) the generic coarse-grained (CG) bead model of polymer physics and (2) a state-of-the-art machine learned atomistic neural network (NN) potential for polyethylene derived from electronic structure calculations. While there is a broad overall agreement between the results on the influence of the pulling rate on chain rupture based on the CG and atomistic NN models, for the simple 31 and 41 knots, significant differences are found for the more complex 51 and 52 knots. Notably, in the latter case, the NN model more frequently predicts that these knots can break not only at the crossings at the entrance/exit but also at one of the central crossing points. The relative smoothness of the CG potential energy surface also leads to stabilization of tighter knots compared to the more realistic NN model.

The Journal of Chemical PhysicsVol. 161(24)
Scuola Internazionale Superiore di Studi Avanzati (IT), Temple University (US)
National Science Foundation, U.S. Department of Energy, European Commission, Army Research Laboratory, DEVCOM Army Research Laboratory
Affordable and clean energy
Openalex Percentile: Top 23%
Force Microscopy Techniques and Applications
1.01
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Molecular dynamics studies of knotted polymers — Michael L. Klein, Mark DelloStritto, et al. · The Journal of Chemical Physics (2024) | TGRS Research Map | TGRS