Machine learning phases of active matter

TL;DR

This paper demonstrates that convolutional neural networks can effectively classify phases and identify transition points in the Vicsek model, surpassing traditional order parameters in accuracy and capability.

Contribution

The study introduces a CNN-based method for phase classification in active matter, successfully identifying phase transitions where traditional methods fail.

Findings

CNN achieves high accuracy in phase classification

All phase transitions are confirmed to be first-order

Traditional order parameters are insufficient for phase identification

Abstract

Recent years have witnessed a growing interest in using machine learning to predict and identify phase transitions in various systems. Here we adopt convolutional neural networks (CNNs) to study the phase transitions of Vicsek model, solving the problem that traditional order parameters are insufficiently able to do. Within the large-scale simulations, there are four phases, and we confirm that all the phase transitions between two neighboring phases are first-order. We have successfully classified the phase by using CNNs with a high accuracy and identified the phase transition points, while traditional approaches using various order parameters fail to obtain. These results indicate that the great potential of machine learning approach in understanding the complexities in collective behaviors, and in related complex systems in general.