论文标题
活性粘性颗粒的低衰减自组装方案
Low-dissipation self-assembly protocols of active sticky particles
论文作者
论文摘要
我们使用神经进化学习来识别与相互作用的通用活性粒子模型中低衰减自组装的时间相关方案。当分配给组装的时间足够长时，低径流方案仅使用颗粒景点，从而产生一定数量的熵，以缩放为颗粒的数量。当时间太短以至于允许组装通过扩散运动进行时，低径流组件协议需要粒子自我启动，产生一定数量的熵，该熵与颗粒数量和引起组装所需的游泳长度缩放。因此，自我塑性为诱导时间的本质而言提供了一种昂贵但必要的机制。
We use neuroevolutionary learning to identify time-dependent protocols for low-dissipation self-assembly in a model of generic active particles with interactions. When the time allotted for assembly is sufficiently long, low-dissipation protocols use only interparticle attractions, producing an amount of entropy that scales as the number of particles. When time is too short to allow assembly to proceed via diffusive motion, low-dissipation assembly protocols instead require particle self-propulsion, producing an amount of entropy that scales with the number of particles and the swim length required to cause assembly. Self-propulsion therefore provides an expensive but necessary mechanism for inducing assembly when time is of the essence.
