Self-assembly is a fundamental process in biology and a central goal in nanotechnology, enabling the formation of complex structures from simple building blocks. While biological systems like proteins rely on intricate, patch-specific interactions to form ordered assemblies, synthetic approaches often struggle with controlling structure size and shape—especially at larger scales. In this talk, we explore how complexity in interactions, even among identical subunits, can give rise to a surprisingly rich variety of ordered structures including crystals, fibers, and oligomers. Through a combination of Monte Carlo simulations, machine learning, and DNA origami experiments, we show that the interplay between periodicity, geometrical frustration, and defect-promoting interactions can be harnessed to both predict and control self-assembly outcomes. These findings reveal fundamental principles that govern structure formation in both natural and synthetic systems and offer new design strategies for building robust, functional nano-architectures.
Marco Baiesi, Daniel Busiello