Learning to control active matter
Learning to control active matter
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DOI：
10.1103/physrevresearch.3.033291
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发表时间：
2021-09-30
影响因子：
4.2
通讯作者：
Murugan, Arvind
中科院分区：
文献类型：
--
作者：
Falk, Martin J.;Alizadehyazdi, Vahid;Murugan, Arvind
对活性物质的研究揭示了新的非平衡集体行为，说明了它们作为新材料平台的潜力。然而，大多数工作将活性物质视为具有均匀微观能量输入的未调节系统，我们称之为活性。相比之下，生物材料中的功能性是通过在空间和时间上局部调节和控制活性而产生的，这在最近的实验中才成为工程活性物质的可能。设计功能需要对时空活动模式的高维空间进行导航，但如果没有系统特定的直觉，蛮力方法就不太可能成功。在这里，我们将强化学习应用于诱导特定方向的净运输的任务，该任务是使用局部增加活动的聚光灯来模拟类似Vicsek的自推进磁盘系统。由此产生的随时间变化的活动模式学习利用强耦合和弱耦合制度的不同物理。我们的工作展示了强化学习如何揭示物理上可解释的协议，用于控制非平衡系统中的集体行为。
The study of active matter has revealed novel non-equilibrium collective behaviors, illustrating their potential as a new materials platform. However, most work treat active matter as unregulated systems with uniform microscopic energy input, which we refer to as activity. In contrast, functionality in biological materials results from regulating and controlling activity locally over space and time, as has only recently become experimentally possible for engineered active matter. Designing functionality requires navigation of the high-dimensional space of spatio-temporal activity patterns, but brute force approaches are unlikely to be successful without system-specific intuition. Here, we apply reinforcement learning to the task of inducing net transport in a specific direction for a simulated system of Vicsek-like self-propelled disks using a spotlight that increases activity locally. The resulting time-varying patterns of activity learned exploit the distinct physics of the strong and weak coupling regimes. Our work shows how reinforcement learning can reveal physically interpretable protocols for controlling collective behavior in non-equilibrium systems.