# Unsupervised learning for local structure detection in colloidal systems

## TL;DR

This paper presents an unsupervised learning algorithm that detects local structures in colloidal systems with high accuracy, using autoencoders and Gaussian mixture models to classify environments without manual tuning.

## Contribution

The authors develop a simple, fast, and adaptable unsupervised method for local environment detection in colloids, outperforming traditional manual approaches.

## Key findings

- Accurately classifies local environments in diverse colloidal systems.
- Identifies key bond-orientational order parameters relevant to each system.
- Works effectively across isotropic, anisotropic, and mixed systems.

## Abstract

We introduce a simple, fast, and easy to implement unsupervised learning algorithm for detecting different local environments on a single-particle level in colloidal systems. In this algorithm, we use a vector of standard bond-orientational order parameters to describe the local environment of each particle. We then use a neural-network-based autoencoder combined with Gaussian mixture models in order to autonomously group together similar environments. We test the performance of the method on snapshots of a wide variety of colloidal systems obtained via computer simulations, ranging from simple isotropically interacting systems, to binary mixtures, and even anisotropic hard cubes. Additionally, we look at a variety of common self-assembled situations such as fluid-crystal and crystal-crystal coexistences, grain boundaries, and nucleation. In all cases, we are able to identify the relevant local environments to a similar precision as "standard", manually-tuned and system-specific, order parameters. In addition to classifying such environments, we also use the trained autoencoder in order to determine the most relevant bond orientational order parameters in the systems analyzed.