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Learning to Classify Threading in Melts of Rings — arXiv:2610.01621

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.01621
License (as archived): CC0-1.0
Archived: 2026-10-07T12:07:09.680Z
SHA-256 of the archived text: c7c8ded52610a2c84f225436e301b08b14601dfe77d8c4fa50e916427f340598
Stable URL of this page: https://metraipolis.com/documento/dc_38ac104f8236

How to cite

Learning to Classify Threading in Melts of Rings — arXiv:2610.01621 — arXiv (en), https://arxiv.org/abs/2610.01621 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_38ac104f8236 (Metraipolis archive dc_38ac104f8236, sha256 c7c8ded52610a2c84f225436e301b08b14601dfe77d8c4fa50e916427f340598)

BibTeX

@misc{dc38ac104f8236,
  title = {Learning to Classify Threading in Melts of Rings — arXiv:2610.01621},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.01621},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_38ac104f8236; sha256 c7c8ded52610a2c84f225436e301b08b14601dfe77d8c4fa50e916427f340598},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.01621. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Dense melts of nonconcatenated ring polymers exhibit anomalous dynamics and nonlinear rheology, in some cases linked to long-lived inter-ring threading entanglement. Detecting these geometric constraints remains computationally demanding, with different methods being based on different geometric features tuned to specific models. Here, we introduce a machine-learning framework that detects threading directly from the writhe of ring conformations and we demonstrate its broad generalisability. Trained on simulations of isolated, distance-constrained pairs of short rings, a one-dimensional convolutional neural network (CNN1D) operating on the writhe profile classifies threading states with over 98% accuracy on held-out ring pairs. Remarkably, this model generalises without retraining to equilibrium dense melts spanning a wide range of densities and chain lengths (N = 100 to 1600), maintaining true-positive rates above 91% with false-positive rates near zero. Misclassified conformations are limited to physically ambiguous, shallow threading events which are arguably not impacting the dynamics of the rings. Effectively, writhe captures the interaction between the two rings, allowing writhe-based classifiers to improve the topological analysis of large ring-polymer systems with a 3-fold speed-up over the state-of-the-art minimal-surface detection. These results establish the writhe-trained neural networks as an accurate, scalable tool for characterising entanglement in ring polymer melts.

Tema degli esploratori: fisica e astronomia (retro-catalogo arXiv: annunci del 2026-10-02 in cond-mat.dis-nn).
Categorie arXiv: Soft Condensed Matter (cond-mat.soft) ; Disordered Systems and Neural Networks (cond-mat.dis-nn).
Pagina del lavoro: https://arxiv.org/abs/2610.01621 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-07T11:37:54.297Z dalla pagina https://arxiv.org/catchup/cond-mat.dis-nn/2026-10-02?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).