Mapping the Phase Diagram of the Vicsek Model with Machine Learning
This provides a systematic method for converting sparse simulation data into global phase diagrams for collective-motion models, though the approach is domain-specific and incremental.
The study uses machine learning to classify and interpolate the phase diagram of the Vicsek flocking model, achieving 92% classification accuracy and resolving a narrow coexistence region between ordered and disordered phases.
In this study, we use machine learning to classify and interpolate the phase structure of the Vicsek flocking model across the three-dimensional parameter space $(η,ρ,v_0)$. We construct a dataset of simulated parameter points and characterize each point using long-time dynamical observables. These observables are then used as inputs to a K-Means clustering procedure, which assigns each point to a disorder, order, or coexistence phase. Using these clustered labels, we train a neural-network classifier to learn the mapping from model parameters to phase behavior, achieving a classification accuracy of 0.92. The resulting phase map resolves a narrow coexistence region separating the ordered and disordered phases and extends the inferred phase boundaries beyond the originally sampled simulation points. More broadly, this approach provides a systematic way to convert sparse simulation data into a global phase diagram for collective-motion models.