Work

Unsupervised classification of disordered patterns in an oppositely charged colloidal system

Year: 2025

Type: article

Abstract: 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.

Source: Physical review. E

DOI: 10.1103/nhyy-qzc8

Authors: Yoshitaka Miyahara, Taiki Haga

Institution: Osaka Metropolitan University

Language: English

Topic: Electrostatics and Colloid Interactions

Subfield: Physical and Theoretical Chemistry

Field: Chemistry

Domain: Physical Sciences

Open Access status: green

Funders: Japan Science and Technology Corporation, Japan Society for the Promotion of Science

Awards: G2135868886, Theory of multiscale decoherence in open quantum many-body systems, G6187309307