标题： 无监督的异种电荷胶体系统中无序图案的分类 显示英文标题

标题： Unsupervised classification of disordered patterns in an oppositely charged colloidal system

摘要： 我们开发了一种无监督机器学习方法，用于分类相反电荷胶体系统中的无序相。 在这个系统中，库仑相互作用和范德华相互作用之间的相互作用导致局部结构的转变，而全局结构保持无序。 我们的方法涉及将系统的局部结构表示为高维向量，并应用主成分分析来识别每个相的不同特征。 我们证明，该方法能对无序相进行合理的分类，这与从径向分布函数获得的结果一致。 该方法的可解释性揭示了每个相的关键特征，并提供了关于非常规相变机制的重要见解。 显示英文摘要

摘要： We develop an unsupervised machine learning approach to classify disordered phases in a system of oppositely charged colloids. In this system, the interplay between Coulomb and van der Waals interactions leads to transitions in local structures, while the global structure remains disordered. Our method involves representing the local structures of the system as high-dimensional vectors and applying principal component analysis to identify distinct features of each phase. We demonstrate that our method results in a reasonable classification of disordered phases, which is consistent with that obtained from radial distribution functions. The interpretability of the method reveals the key characteristics of each phase and provides valuable insights into the mechanisms underlying the unconventional phase transitions.