Dynamic control of self-assembly of quasicrystalline structures through reinforcement learning
arXiv:2309.06869 · doi:10.1039/D4SM01038H
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.
arXiv comment11 pages, 12 figures, includes Supplementary Information
References in corpus (17)
- Evolution Strategies as a Scalable Alternative to Reinforcement Learning
- Multidimensional replica-exchange method for free-energy calculations
- Extended Ensemble Monte Carlo
- Quantum critical state in a magnetic quasicrystal
- Self-Assembly of Monatomic Complex Crystals and Quasicrystals with a Double-Well Interaction Potential
- Formation of dodecagonal quasicrystals in two-dimensional systems of patchy particles
- Optimal Control of Active Nematics
- Learning to grow: control of material self-assembly using evolutionary reinforcement learning
- Computing phase diagrams for a quasicrystal-forming patchy-particle system
- Learning to flock through reinforcement
- Learning to Control Active Matter
- Reinforcement learning of optimal active particle navigation
- Temperature Protocols to Guide Selective Self-Assembly of Competing Structures
- Inverse design of charged colloidal particle interactions for self assembly into specified crystal structures
- Inverse design of two-dimensional structure by self-assembly of patchy particles
- Bayesian Modelling of Pattern Formation from One Snapshot of Pattern
- Formation and Fluctuation of Two-dimensional Dodecagonal Quasicrystal