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

TL;DR

This paper uses reinforcement learning to dynamically control temperature in the self-assembly process of quasicrystals, successfully reducing defects and guiding the system toward stable structures.

Contribution

It introduces a reinforcement learning approach to optimize temperature protocols for self-assembly of quasicrystals, demonstrating autonomous discovery of effective control strategies.

Findings

Reinforcement learning identifies a characteristic temperature that enhances structural stability.

The learned policy reduces defects in the assembled quasicrystal structures.

RL effectively guides the system toward metastable and unstable targets.

Abstract

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.