Classification of emerging patterns in self-assembled two-dimensional magnetic lattices

Area of Science:

• Physics
• Materials Science
• Complex Systems

Background:

• Self-assembled granular materials offer tunable properties for applications like shock absorption and energy harvesting.
• The statistical nature of self-assembly presents challenges in achieving repeatable and stable ordered structures.
• Controlling emergent patterns in discrete systems is crucial for material design.

Purpose of the Study:

• To numerically and experimentally investigate the 2D self-assembly of magnetic disks within confined boundaries.
• To develop and validate an agent-based model for predicting self-assembled patterns.
• To classify emergent patterns and characterize their order and crystallinity.

Main Methods:

• An agent-based model was developed to simulate the self-assembly of free-floating disks with repulsive magnetic potentials.
• Numerical simulations were conducted considering six disk types and seven boundary shapes.
• Experimental verification was performed to validate the model's predictions.

Main Results:

• The agent-based model successfully predicts self-assembled patterns, though solutions can be non-unique and depend on initial conditions.
• Emergent patterns were classified into monostable (independent of initial conditions) and multistable types.
• The study characterized the emergent order and crystallinity of the self-assembled structures.

Conclusions:

• The developed model provides a predictive framework for designing self-assembled granular materials.
• The ability to control and predict emergent patterns is key for creating re-programmable materials.
• This research opens avenues for materials with exceptional nonlinear properties and tailored functionalities.