Unsupervised learning for local structure detection in colloidal systems

Understand

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.