用主动学习高效绘制维克塞克模型相图，揭示复杂有序态的物理机制。
Active-learning mapping of the Vicsek model phase diagram
- 基于神经网络分类器与熵值选择，智能规划高不确定性区域的模拟实验。
- 发现中等密度下存在密度-有序共存的中间相，其噪声窗口随密度升高而上移拓宽。
- 结合空间诊断，将机器学习划分的相区转化为可物理解读的带状结构与局域有序差异。
维克塞克模型是群体运动的最小模型，描述局部对齐如何产生宏观非平衡有序态，如鸟群行为。本文采用主动学习方法，以噪声强度、密度和粒子速度为参数，绘制该模型的相图。训练一个神经网络分类器识别全局极化序，并利用分类器熵值选择不确定性高的区域进行新模拟。结果揭示了高噪声无序气体相、低噪声极化有序相，以及一个中间共存候选相；该中间相的噪声窗口随密度增加而上移并变宽。独立的密度与局域有序诊断表明，中间相包含密集的局域有序条带与稀疏弱有序背景共存。与有序相比较发现，条带对比度增强、局域有序异质性上升及密度-有序正相关，共同构成共存特征。总体而言，本工作建立了一套机器学习引导的主动物质研究流程：主动学习构建操作相图，空间诊断将其转化为可物理解读的非平衡形态。
原文摘要 · Abstract (English)
The Vicsek model is a minimal model of collective motion, capturing how local alignment interactions can generate macroscopic nonequilibrium order in systems such as bird flocks. In this work, we use active learning to map the Vicsek phase diagram as a function of noise strength, density, and particle speed. A neural-network classifier is trained on global polar-order labels, and classifier entropy is used to select new simulations near uncertain crossover regions. The resulting phase map resolves a high-noise disordered gas, a low-noise polar ordered regime, and an intermediate coexistence-candidate regime whose noise window shifts upward and broadens with increasing density. Independent density and local-order diagnostics indicate that the intermediate regime contains dense, locally ordered bands coexisting with a dilute, weakly ordered background. Comparison with the ordered regime shows that banded coexistence is identified by the joint enhancement of band contrast, local-order heterogeneity, and positive density-order correlation. Overall, these results establish a machine-learning-guided workflow for active matter, in which active learning constructs an operational phase map and independent spatial diagnostics convert classifier-defined regimes into physically interpretable nonequilibrium morphologies.
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