Prof. Robin Selinger, Advanced Materials and Liquid Crystal Institute, Kent State University

"Modeling shape transformations in liquid crystal elastomers: a machine learning approach to inverse design"

Abstract: Liquid Crystal Elastomers (LCE) are stimuli-responsive, programmable actuators that undergo shape-morphing in response to a change of temperature, illumination, or other environmental cues. The resulting actuation trajectory is programmed by patterning the nematic director field, e.g. by forming the sample between glass substrates with prescribed surface anchoring patterns. The two surfaces may have identical anchoring patterns; or if they are different, we determine the resulting complex microstructure by minimizing the Frank-Oseen free energy. Using a GPU-based finite element simulation developed in-house, we explore mechanisms by which LCE thin coatings give rise to stimulus-driven transformations in surface topography. We investigate director microstructures containing arrays of topological defects and develop a machine learning algorithm to optimize the shape of resulting topological features, for instance to create a surface with suction cups like those of an octopus. In separate work, we describe our recent discovery that the Frank-Read source mechanism, which drives emission of concentric dislocation loops in crystalline solids, can likewise drive emission of disclination loops in a nematic liquid crystal. We discuss potential implications for controlling microstructural evolution in passive and active nematic liquid crystals.