Whitelam Stephen, Schmit Jeremy D
Molecular Foundry, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720, USA.
Department of Physics, Kansas State University, Manhattan, KS, 66506, USA.
J Cryst Growth. 2022 Dec 15;600. doi: 10.1016/j.jcrysgro.2022.126912. Epub 2022 Oct 13.
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.
我们使用神经进化学习来识别具有相互作用的通用活性粒子模型中低耗散自组装的时间相关协议。当分配给组装的时间足够长时，低耗散协议仅使用粒子间吸引力，产生的熵量与粒子数量成比例。当时间过短以至于无法通过扩散运动进行组装时，低耗散组装协议则需要粒子自我推进，产生的熵量与粒子数量以及导致组装所需的游动长度成比例。因此，当时间至关重要时，自我推进为诱导组装提供了一种昂贵但必要的机制。
