Dynamic control of self-assembly of quasicrystalline structures through reinforcement learning
This work addresses the challenge of kinetic pathway control in material science for improved quasicrystal synthesis, representing an incremental advance by applying RL to a known bottleneck.
The researchers tackled the problem of controlling the self-assembly of dodecagonal quasicrystals from patchy particles by using reinforcement learning to optimize temperature control, resulting in the generation of structures with few defects and the discovery of a characteristic temperature that enhances formation.
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. 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 DDQC. We also illustrate the performance of RL when the target is metastable or unstable. To clarify the success of the learning, we analyse a simple model describing the kinetics of structural changes through the motion in a triple-well potential.