Dynamic control of self-assembly of quasicrystalline structures through reinforcement learning

We propose reinforcement learning to control the dynamical self-assembly of a dodecagonal quasicrystal (DDQC) from patchy particles. Patchy particles undergo anisotropic interactions with other particles and form DDQCs. However, their structures in steady states are significantly influenced by the kinetic pathways of their structural formation. We estimate the best temperature control policy using the Q-learning method and demonstrate its effectiveness in generating DDQCs with few defects. It is found that reinforcement learning autonomously discovers a characteristic temperature at which structural fluctuations enhance the chance of forming a globally stable state. The estimated policy guides the system toward the characteristic temperature to assist the formation of DDQCs. We also illustrate the performance of RL when the target is metastable or unstable.