Rational Design of Soft Hierarchical Materials with Responsive Functionalities: Machine learning Soft Matter to create Soft Machines

ongoing

Nature displays fascinating examples of self-assembled materials that reconfigure and respond to external stimuli, e.g. chameleons change color for camouflage, pine cones release seeds upon a change in humidity. Advances in colloid synthesis have resulted in a diversity of self-assembled nanostructures with interesting functional properties. These nanostructures are however passive! The aim of this project is to explore the new physics that emerges when static nanostructures are elastically coupled to a soft elastic matrix or hydrogel, e.g. nanoparticles with (cross-linked) ligands, core-shell microgel particles. These hydrogels can be actuated by pH, temperature, light, resulting in a (de)swelling of the gel and a reconfiguration of the nanostructure. Reconfigurable dynamic materials are interesting for applications, but their rational design remains a major challenge as it requires a detailed comprehension of the highly non-trivial coordination of dynamic behaviors of materials across different time and length scales. Using extensive simulations, coarse-graining and machine learning, I propose to unravel the microscopic origin of the structural and dynamic behavior of soft reconfigurable materials. I will build coarse-grained models at multiple levels to study the structure and properties of these soft materials. I will then investigate the dynamics and shape transformation kinetics of the nanostructure and hydrogel upon actuation. The final goal is to reverse-engineer using evolutionary algorithms new classes of soft responsive materials from the atomic scale by designing colloids that self-assemble at the mesoscale into large-scale structures, to the macroscopic scale by tailoring the shape-morphing properties. This research will produce unprecedented insight, novel simulation methods, and fundamental models for the rational design of soft responsive materials that arise from the hierarchical assembly of structures and their dynamic behaviors across scales.