Unsupervised classification of disordered patterns in an oppositely charged colloidal system

We develop an unsupervised machine learning approach to classify disordered patterns 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 pattern. We demonstrate that our method results in a reasonable classification of disordered patterns, which is consistent with that obtained from radial distribution functions. The interpretability of the method reveals the key characteristics of each pattern and provides valuable insights into the mechanisms underlying the unconventional phase transitions between disordered patterns.