Rapid Discovery of Sequence‐Encoded Magnetically Reconfigurable Microrobots Using Monte Carlo Simulations

Reconfigurable microrobots promise advancements in microsurgical tools, self‐healing materials, and environmental remediation by enabling precise, adaptive functionalities at small scales. However, predicting their behaviors a priori remains a significant challenge, limiting the pace of design and discovery. To address this, a Monte Carlo simulation framework is presented for predicting the folding behavior of self‐assembled, sequence‐encoded microrobot chains composed of magnetic particles, enabling efficient exploration of their large design space. This computational framework employs metrics of radius of gyration, tortuosity, and symmetry score to map the design space, identify functional sequences, and predict likely folding behaviors before fabrication. The framework through experiments to demonstrate accuracy in capturing folding behaviors is validated. Statistical analysis reveals adherence to self‐avoiding walk principles from polymer theory, providing a foundation for understanding how input sequences drive folding capabilities. Moreover, the simulation surpasses current experimental capabilities, enabling exploration of novel microrobot designs, such as sequences incorporating mixtures of cubes and triangular prism subunits, which exhibit distinct compressive behaviors. Beyond the sequence‐encoded microrobots investigated in this study, this framework offers broad utility for the design of reconfigurable microscale systems by reducing reliance on experimental prototyping and accelerating discovery of new functional microrobots for use in biomedicine, materials engineering, and sustainability.