Writhe-Based Polymer Link Classification Using Machine Learning
Unique and rapid classification of knots and links is an open mathematical problem that is relevant to a range of (bio)physical systems, including polymer melts, DNA, and proteins. In this paper, we explore a data-driven approach to the classification problem of link topology. Extending the framework introduced in Ref. 1 (Sleiman et al, 2024 Soft Matter, 20(1), pp.71-78), we show that a feedforward neural network trained on the writhe density matrix classifies thermally equilibrated configurations of the first six prime links with 97% accuracy. We demonstrate that this accuracy remains high across a range of temperatures and lengths of link components, while rapidly deteriorating with the...
| Authors | Jack Beda, Djordje Mihajlovic, Kasturi Barkataki et al. |
|---|---|
| Primary category | math.GT |
| Published | 22 July 2026 |
| Last updated at source | 22 July 2026 |
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