PULS Group, Institute for Theoretical Physics, Interdisciplinary center for nanostructured films (IZNF), Friedrich-Alexander-Universität Erlangen-Nürnberg, Cauerstr. 3, 91058, Erlangen, Germany.
Laboratoire de Physique et Mécanique des Milieux Hétérogènes, CNRS UMR 7636, ESPCI Paris et PSL Université, 10 rue Vauquelin, 75005, Paris, France.
Nat Commun. 2022 Jul 27;13(1):4357. doi: 10.1038/s41467-022-31736-z.
Information storage is a key element of autonomous, out-of-equilibrium dynamics, especially for biological and synthetic active matter. In synthetic active matter however, the implementation of internal memory in self-propelled systems is often absent, limiting our understanding of memory-driven dynamics. Recently, a system comprised of a droplet generating its guiding wavefield appeared as a prime candidate for such investigations. Indeed, the wavefield, propelling the droplet, encodes information about the droplet trajectory and the amount of information can be controlled by a single scalar experimental parameter. In this work, we show numerically and experimentally that the accumulation of information in the wavefield induces the loss of time correlations, where the dynamics can then be described by a memory-less process. We rationalize the resulting statistical behavior by defining an effective temperature for the particle dynamics where the wavefield acts as a thermostat of large dimensions, and by evidencing a minimization principle of the generated wavefield.
信息存储是自主、非平衡动力学的关键要素，特别是对于生物和合成活性物质。然而，在合成活性物质中，自推进系统中内部记忆的实现往往是缺失的，这限制了我们对记忆驱动动力学的理解。最近，由产生导向波场的液滴组成的系统似乎是此类研究的首选。实际上，推动液滴的波场编码了有关液滴轨迹的信息，并且信息的数量可以通过单个标量实验参数来控制。在这项工作中，我们通过数值和实验证明了波场中信息的积累会导致时间相关性的丧失，其中动力学可以由无记忆过程来描述。我们通过为粒子动力学定义一个有效温度来合理化由此产生的统计行为，其中波场充当大尺寸的恒温器，并通过证明生成的波场的最小化原理来证明这一点。
