A central paradigm of self-assembly is to create ordered structures starting from molecular monomers that spontaneously recognize and interact with each other via noncovalent interactions. In recent years, great efforts have been directed toward perfecting the design of a variety of supramolecular polymers and materials with different architectures. The resulting structures are often thought of as ideally perfect, defect-free supramolecular fibers, micelles, vesicles, etc., having an intrinsic dynamic character, which are typically studied at the level of statistical ensembles to assess their average properties. However, molecular simulations recently demonstrated that local defects that may be present or may form in these assemblies, and which are poorly captured by conventional approaches, are key to controlling their dynamic behavior and properties. The study of these defects poses considerable challenges, as the flexible/dynamic nature of these soft systems makes it difficult to identify what effectively constitutes a defect and to characterize its stability and evolution. Here, we demonstrate the power of unsupervised machine-learning techniques to systematically identify and compare defects in supramolecular polymer variants in different conditions, using as a benchmark 5 Å resolution coarse-grained molecular simulations of a family of supramolecular polymers. We show that this approach allows a complete data-driven characterization of the internal structure and dynamics of these complex assemblies and of the dynamic pathways for defects formation and resorption. This provides a useful, generally applicable approach to unambiguously identify defects in these dynamic self-assembled materials and to classify them based on their structure, stability, and dynamics.

中文翻译：

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识别和跟踪动态超分子聚合物中的缺陷。

自组装的中心范例是从分子单体开始创建有序结构，这些分子单体通过非共价相互作用自发识别并相互作用。近年来，人们致力于将各种具有不同结构的超分子聚合物和材料的设计完善。通常将得到的结构视为具有内在动力学特性的理想的完美，无缺陷的超分子纤维，胶束，囊泡等，通常在统计集合层次上对其进行研究以评估其平均性能。但是，最近的分子模拟表明，这些组件中可能存在或可能形成局部缺陷，而传统方法很难捕捉到这些缺陷，是控制其动态行为和属性的关键。这些缺陷的研究提出了相当大的挑战，因为这些软系统的柔韧性/动态特性使其难以识别有效构成缺陷的要素并难以表征其稳定性和演化。在这里，我们展示了无监督机器学习技术的强大功能，可以使用超分子聚合物家族的5分辨率粗粒度分子模拟作为基准，在不同条件下系统地识别和比较超分子聚合物变异体中的缺陷。我们表明，这种方法可以对这些复杂组件的内部结构和动力学以及缺陷形成和吸收的动态路径进行完整的数据驱动表征。这提供了有用的