Description:

Experiments have reached a monumental capacity for designing, synthesizing, and controlling microscopic particles for self-assembly. However, our ability to take full-advantage of this vast design space to assemble nanomaterials with complex structure and function is hindered by the lack of inverse-design frameworks that connect the particle-level design attributes to the system-level assembly outcomes, like the yield of a user-specified target structure. I will show that equilibrium self-assembly is governed by an underlying mathematical architecture that robustly predicts which structures can be assembled at high yield, as well as how tuning the design-space parameters dictates the relative yields of competing structures.  This framework leads to a series of quantitative predictions, such as the maximum possible yield that can be achieved for any target structure, which we verify through multifarious assembly experiments of nanoscale particles synthesized using DNA origami. Finally, I will discuss how this presents a practical and robust tool for the rational design of self-assembly, and speculate on its implications for biological systems.  

Carl Goodrich, Assistant Professor, Institute of Science and Technology, Austria

Host: Michelle Driscoll