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用机器学习和噪声来描述活性物质中不同的运动诱导模式

D McDermott¹, C Reichhardt², C J O Reichhardt²

¹X-Theoretical Design Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA.

Physical review. E

|January 20, 2024

概括

活性物质系统中的移动性诱导相分离 (MIPS) 揭示了不同的流体,晶体和临界状态. 机器学习和噪声分析有效地描述了这些动态状态,为集体行为提供了新的见解.

科学领域:

物理物理学物理
软物质物理学软物质物理学
统计力学统计力学

背景情况:

活性物质系统表现出在平衡系统中看不到的新兴集体行为.
运动诱导相分离 (MIPS) 是活性物质中的一个关键现象,导致自我组织.
了解MIPS中的不同阶段和转变对于预测活性物质行为至关重要.

研究的目的:

在2D运行和的磁盘系统中研究动力诱导相分离 (MIPS) 内的不同模式.
使用新的分析方法,区分活性流体,活性晶体和关键状态.
将机器学习和噪声波动分析与传统措施的有效性进行比较.

主要方法:

利用机器学习算法和噪声波动分析来研究MIPS.
从主要组件分析与集群稳定性测量相结合开发了一个顺序参数.
分析了平均速度波动的噪声功率光谱.

主要成果:

在MIPS中确定了三种不同的模式:活性流体,活性晶体和临界.
主要组件衍生订单参数有效地区分了这些方案.
与最大集群大小等结构性措施相比,机器学习证明了动态性质的优越捕获.
噪声功率光谱在关键状态中表现出特有的1/f^{1.6}签名.

结论:

该研究成功地使用先进的分析技术在MIPS中区分和描述了不同的制度.
机器学习和噪声分析为理解活性物质复杂动态提供了强大的工具.
这些发现为MIPS提供了更细致的观点,揭示了与凝聚物质物理学相似的关键行为.

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