Prof. Dr. Robin Selinger (Kent State U): Modeling shape transformations in liquid crystal elastomers: a machine learning approach to inverse design

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 which may be identical or entirely different. Using a GPU-based finite element simulation developed in-house, we explore mechanisms by which arrays of topological defects in the microstructure of LCE thin coatings give rise to complex transformations in surface topography. We also develop a machine learning algorithm to optimize the shape of resulting topological features. 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.
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