| 研究実績の概要 |
We propose reinforcement learning to control the dynamical self-assembly of the dodecagonal quasicrystal from patchy particles. The patchy particles have anisotropic interactions with other particles and form DDQC. 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 dodecagonal quasicrystal structures 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.
In the future plan, applying reinforcement learning to control the properties of the assembled structure is a possible direction to investigate the underlying relation between the structure and property.
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