We propose reinforcement learning to control the dynamical self-assembly of the dodecagonal quasicrystal (DDQC) from patchy particles. The patchy particles have anisotropic interactions with other particles and form DDQC. However, their structures at steady states are significantly influenced by the kinetic pathways of their structural formation. We estimate the best policy of temperature control trained by the Q-learning method and demonstrate that we can generate DDQC with few defects using the estimated policy. The temperature schedule obtained by reinforcement learning can reproduce the desired structure more efficiently than the conventional pre-fixed temperature schedule, such as annealing. To clarify the success of the learning, we also analyse a simple model describing the kinetics of structural changes through the motion in a triple-well potential. We have found that reinforcement learning autonomously discovers the critical temperature at which structural fluctuations enhance the chance of forming a globally stable state. The estimated policy guides the system toward the critical temperature to assist the formation of DDQC.

我们提出了强化学习来控制补丁颗粒的动力自组装十二边形准晶体（DDQC）。研究发现，通过训练温度控制的最佳策略，我们能够使用估计的策略生成缺陷较少的DDQC。与传统的固定温度表如退火等相比，通过强化学习获得的温度计划能更高效地复现所需的结构。同时，我们发现强化学习能够自主地发现结构波动增强实现全局稳态的临界温度。估计的策略引导系统朝向临界温度以促进DDQC的形成。

通过强化学习动态控制准晶结构的自组装