(704b) Programmed Assembly of Anisotropic Patchy Colloids By Nonlinear Learning and Landscape Engineering | AIChE

(704b) Programmed Assembly of Anisotropic Patchy Colloids By Nonlinear Learning and Landscape Engineering


Ferguson, A. L. - Presenter, University of Illinois at Urbana-Champaign
Long, A. W., University of Illinois at Urbana-Champaign
The inherently many-body nature of assembly makes it a challenge to identify the underlying collective mechanisms and assembly pathways, and has hindered the rational design of building blocks to produce aggregates with desired structure and function. We have combined tools from statistical mechanics, nonlinear machine learning, and molecular simulation to develop an approach to determine low-dimensional assembly landscapes preserving the important thermodynamics (what stable aggregates can assemble?) and mechanisms (how do they assemble?) of assembly. We have applied this approach to understand and predict the assembly of anisotropic patchy colloids, Janus particles, "digital colloids", and asphaltene molecules.

In this work we build on these foundations to perform inverse design of colloidal building blocks to spontaneously self-assemble into aggregates with desired structure. We engage this challenge through the principle of "landscape engineering" – rational sculpting of the low-dimensional assembly landscape to maximally favor the target morphology. Implementing this concept in practice, we couple our nonlinear machine learning approach with state-of-the-art optimization engines to iteratively optimize building block properties to maximize the thermodynamic stability and kinetic accessibility of the target structure. We demonstrate our approach in the inverse design of anisotropic patchy colloids engineered to assemble hollow Platonic polyhedra with applications as biomimetic models of viral capsids and in the hierarchical assembly of 3D photonic crystals. In an application to icosahedral target aggregates, our approach discovers a colloid architecture that produces ~45% improved yields than that designed by expert knowledge and geometric concerns alone. Starting from this optimized icosahedral design and selecting octahedra as our target, our approach is capable of efficiently mutating the building block design from this poor starting guess to engineer a new colloid programmed to assemble into octahedra with high yield and selectivity.