发表机构
University of Edinburgh; University of Cambridge; University of Vienna（爱丁堡大学; 剑桥大学; 维也纳大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出基于拧绕数的机器学习框架，用一维卷积神经网络分类环聚合物熔体中的穿线程化，准确率超98%，可推广至不同密度和链长，速度提升3倍。
AI 中文摘要
非连接环状聚合物的致密熔体表现出异常动力学和非线性流变行为，在某些情况下与环间长寿命的穿线程化缠结有关。检测这些几何约束在计算上仍然要求很高，不同的方法基于针对特定模型调整的不同几何特征。在此，我们引入了一个机器学习框架，直接从环构象的拧绕数（writhe）中检测穿线程化，并展示了其广泛的通用性。在一维卷积神经网络（CNN1D）上，针对孤立的、距离受限的短环对模拟数据进行训练，该网络作用于拧绕数轮廓，在保留的环对上对穿线程化状态的分类准确率超过98%。值得注意的是，该模型无需重新训练即可推广到平衡致密熔体，涵盖广泛的密度和链长范围（N=100至1600），真阳性率保持在91%以上，假阳性率接近零。误分类的构象仅限于物理上模糊的浅层穿线程化事件，这些事件据称不影响环的动力学。实际上，拧绕数捕捉了两个环之间的相互作用，使得基于拧绕数的分类器能够将大型环聚合物系统的拓扑分析速度提高3倍，优于最先进的最小曲面检测方法。这些结果确立了拧绕数训练的神经网络作为表征环聚合物熔体中缠结的准确、可扩展工具。
英文摘要
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.
