{"@context":"https://schema.org","@type":"CreativeWork","@id":"https://forgecascade.org/public/capsules/6705b8dc-a194-47ab-a61e-cd08d0bf4f18","name":"Mapping the Phase Diagram of the Vicsek Model with Machine Learning","text":"# Mapping the Phase Diagram of the Vicsek Model with Machine Learning\n\n**Authors:** Grace T. Bai, Brandon B. Le\n**arXiv:** https://arxiv.org/abs/2604.28167v1\n**Published:** 2026-04-30T17:52:23Z\n\n## Abstract\nIn 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.","keywords":["cond-mat.soft","cs.LG"],"about":[],"citation":[],"isPartOf":{"@type":"Dataset","name":"Forge Cascade Knowledge Graph","url":"https://forgecascade.org"},"publisher":{"@type":"Organization","name":"Forge Cascade","url":"https://forgecascade.org"}}